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<div class="title">ConvolutionLayer.cpp</div> </div>
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<div class="contents">
-<a href="benchmark_2_c_l_2_convolution_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="_c_l_tensor_8h.xhtml">arm_compute/runtime/CL/CLTensor.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_c_l_tensor_allocator_8h.xhtml">arm_compute/runtime/CL/CLTensorAllocator.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_c_l_convolution_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLConvolutionLayer.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_c_l_accessor_8h.xhtml">tests/CL/CLAccessor.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_convolution_layer_fixture_8h.xhtml">tests/benchmark/fixtures/ConvolutionLayerFixture.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_convolution_layer_dataset_8h.xhtml">tests/datasets/system_tests/alexnet/AlexNetConvolutionLayerDataset.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_convolution_layer_dataset_8h.xhtml">tests/datasets/system_tests/googlenet/inceptionv1/GoogLeNetInceptionV1ConvolutionLayerDataset.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_convolution_layer_dataset_8h.xhtml">tests/datasets/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4ConvolutionLayerDataset.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_convolution_layer_dataset_8h.xhtml">tests/datasets/system_tests/lenet5/LeNet5ConvolutionLayerDataset.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="_squeeze_net_convolution_layer_dataset_8h.xhtml">tests/datasets/system_tests/squeezenet/SqueezeNetConvolutionLayerDataset.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="_v_g_g16_convolution_layer_dataset_8h.xhtml">tests/datasets/system_tests/vgg/vgg16/VGG16ConvolutionLayerDataset.h</a>"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include "<a class="code" href="_y_o_l_o_v2_convolution_layer_dataset_8h.xhtml">tests/datasets/system_tests/yolo/v2/YOLOV2ConvolutionLayerDataset.h</a>"</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</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="l00039"></a><span class="lineno"> 39</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="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include "<a class="code" href="_type_printer_8h.xhtml">utils/TypePrinter.h</a>"</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword">const</span> <span class="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> });</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12"> 51</a></span> <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</a> = <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">ConvolutionLayerFixture<CLTensor, CLConvolutionLayer, CLAccessor></a>;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#acd09bed517e43d28823e69494f259835">TEST_SUITE</a>(CL)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetConvolutionLayer, <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</a>, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</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::AlexNetConvolutionLayerDataset(),</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</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="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(LeNet5ConvolutionLayer, CLConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</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::LeNet5ConvolutionLayerDataset(),</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  data_types),</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#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1ConvolutionLayer, CLConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</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::GoogLeNetInceptionV1ConvolutionLayerDataset(),</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  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>(GoogLeNetInceptionV4ConvolutionLayer, CLConvolutionLayerFixture, 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::GoogLeNetInceptionV4ConvolutionLayerDataset(),</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  data_types),</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</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="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetConvolutionLayer, CLConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</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#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::SqueezeNetConvolutionLayerDataset(),</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  data_types),</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</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="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#acd09bed517e43d28823e69494f259835">TEST_SUITE</a>(NIGHTLY)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetConvolutionLayer, CLConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::NIGHTLY,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</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::AlexNetConvolutionLayerDataset(),</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  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", { 4, 8 })));</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>(LeNet5ConvolutionLayer, <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</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_convolution_layer_dataset.xhtml">datasets::LeNet5ConvolutionLayerDataset</a>(),</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</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="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1ConvolutionLayer, <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</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_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ConvolutionLayerDataset</a>(),</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</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="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV4ConvolutionLayer, <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</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_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ConvolutionLayerDataset</a>(),</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</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="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetConvolutionLayer, <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</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_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(),</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</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#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 4, 8 })));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="comment">// 8 batches use about 1.8GB of memory which is too much for most devices!</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>(VGG16ConvolutionLayer, <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</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_v_g_g16_convolution_layer_dataset.xhtml">datasets::VGG16ConvolutionLayerDataset</a>(),</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 1, 4 })));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(YOLOV2ConvolutionLayer, <a class="code" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">CLConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</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#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_convolution_layer_dataset.xhtml">datasets::YOLOV2ConvolutionLayerDataset</a>(),</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 1, 4, 8 })));</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#a603cb7f45efd81606e51686da9aeebd9">TEST_SUITE_END</a>()</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#a603cb7f45efd81606e51686da9aeebd9">TEST_SUITE_END</a>()</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">arm_compute::test::datasets::SqueezeNetConvolutionLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_squeeze_net_convolution_layer_dataset_8h_source.xhtml#l00056">SqueezeNetConvolutionLayerDataset.h:56</a></div></div>
-<div class="ttc" id="_c_l_convolution_layer_8h_xhtml"><div class="ttname"><a href="_c_l_convolution_layer_8h.xhtml">CLConvolutionLayer.h</a></div></div>
-<div class="ttc" id="_v_g_g16_convolution_layer_dataset_8h_xhtml"><div class="ttname"><a href="_v_g_g16_convolution_layer_dataset_8h.xhtml">VGG16ConvolutionLayerDataset.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_convolution_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_convolution_layer_dataset.xhtml">arm_compute::test::datasets::GoogLeNetInceptionV4ConvolutionLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_goog_le_net_inception_v4_convolution_layer_dataset_8h_source.xhtml#l00061">GoogLeNetInceptionV4ConvolutionLayerDataset.h:61</a></div></div>
+<a href="benchmark_2_c_l_2_convolution_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="_c_l_tensor_8h.xhtml">arm_compute/runtime/CL/CLTensor.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_c_l_tensor_allocator_8h.xhtml">arm_compute/runtime/CL/CLTensorAllocator.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_c_l_g_e_m_m_convolution_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_c_l_winograd_convolution_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_c_l_accessor_8h.xhtml">tests/CL/CLAccessor.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "tests/benchmark/fixtures/ConvolutionLayerFixture.h"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "tests/datasets/system_tests/alexnet/AlexNetConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "tests/datasets/system_tests/googlenet/inceptionv1/GoogLeNetInceptionV1ConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "tests/datasets/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4ConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "tests/datasets/system_tests/lenet5/LeNet5ConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include "tests/datasets/system_tests/mobilenet/MobileNetConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include "tests/datasets/system_tests/squeezenet/SqueezeNetConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include "tests/datasets/system_tests/vgg/vgg16/VGG16ConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include "tests/datasets/system_tests/yolo/v2/YOLOV2ConvolutionLayerDataset.h"</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</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="l00042"></a><span class="lineno"> 42</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="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">#include "<a class="code" href="utils_2_type_printer_8h.xhtml">utils/TypePrinter.h</a>"</span></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><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">namespace </span>test</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">namespace </span>benchmark</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</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_1_1benchmark.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> });</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae"> 56</a></span> <span class="keyword">using</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a> = ConvolutionLayerFixture<CLTensor, CLGEMMConvolutionLayer, CLAccessor>;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#acd09bed517e43d28823e69494f259835">TEST_SUITE</a>(CL)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8"> 60</a></span> using <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8">CLWinogradLayerFixture</a> = WinogradConvolutionLayerFixture<<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="code" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a>, <a class="code" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</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> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetWinogradLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8">CLWinogradLayerFixture</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::AlexNetWinogradLayerDataset(),</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>("ActivationInfo", <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>::ActivationFunction::RELU))),</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>", DataType::F32)),</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</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="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1WinogradLayer, CLWinogradLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::GoogLeNetInceptionV1WinogradLayerDataset(),</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo", <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>::ActivationFunction::RELU))),</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#a352791fb808d42a82ad70df5efa3508b">make</a>("DataType", DataType::F32)),</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_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV4WinogradLayer, CLWinogradLayerFixture, 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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::GoogLeNetInceptionV4WinogradLayerDataset(),</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>("ActivationInfo", <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>())),</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("DataType", DataType::F32)),</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</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="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetWinogradLayer, CLWinogradLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::SqueezeNetWinogradLayerDataset(),</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo", ActivationLayerInfo())),</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#a352791fb808d42a82ad70df5efa3508b">make</a>("DataType", DataType::F32)),</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_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetConvolutionLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a>, 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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::AlexNetConvolutionLayerDataset(), framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo",</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</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="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(LeNet5ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::LeNet5ConvolutionLayerDataset(), framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo", ActivationLayerInfo())),</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  data_types),</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</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="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo",</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  data_types),</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</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="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_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV4ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo",</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  ActivationLayerInfo())),</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  data_types),</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</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="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::SqueezeNetConvolutionLayerDataset(), framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo",</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  data_types),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</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="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_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(MobileNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::MobileNetConvolutionLayerDataset(), framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo",</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  data_types),</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</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="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#acd09bed517e43d28823e69494f259835">TEST_SUITE</a>(NIGHTLY)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::NIGHTLY,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</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>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::AlexNetConvolutionLayerDataset(), framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("ActivationInfo",</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  data_types),</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</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="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_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(LeNet5ConvolutionLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::LeNet5ConvolutionLayerDataset(), <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>())),</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</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="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1ConvolutionLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))),</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</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="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV4ConvolutionLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>())),</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</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="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetConvolutionLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::SqueezeNetConvolutionLayerDataset(), <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>,</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))),</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</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="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="comment">// 8 batches use about 1.8GB of memory which is too much for most devices!</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(VGG16ConvolutionLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::VGG16ConvolutionLayerDataset(), <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))),</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 1, 4 })));</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(YOLOV2ConvolutionLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">CLGEMMConvolutionLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::YOLOV2ConvolutionLayerDataset(), <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>())),</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>),</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 1, 4, 8 })));</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetWinogradLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8">CLWinogradLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::AlexNetWinogradLayerDataset(),</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))),</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <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>)),</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</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="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1WinogradLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8">CLWinogradLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::GoogLeNetInceptionV1WinogradLayerDataset(),</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))),</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <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>)),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</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="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV4WinogradLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8">CLWinogradLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::GoogLeNetInceptionV4WinogradLayerDataset(),</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>())),</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <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>)),</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</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="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetWinogradLayer, <a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8">CLWinogradLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</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="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(datasets::SqueezeNetWinogradLayerDataset(),</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"ActivationInfo"</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))),</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <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>)),</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</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="l00186"></a><span class="lineno"> 186</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#a603cb7f45efd81606e51686da9aeebd9">TEST_SUITE_END</a>()</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#a603cb7f45efd81606e51686da9aeebd9">TEST_SUITE_END</a>()</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> } <span class="comment">// namespace benchmark</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_1_1test_1_1benchmark_xhtml_aab9a2ff74a27ae837d32a79a38952228"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1benchmark.xhtml#aab9a2ff74a27ae837d32a79a38952228">arm_compute::test::benchmark::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#l00042">DepthwiseConvolutionLayer.cpp:42</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( ) </div></div>
<div class="ttc" id="_c_l_tensor_8h_xhtml"><div class="ttname"><a href="_c_l_tensor_8h.xhtml">CLTensor.h</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="_y_o_l_o_v2_convolution_layer_dataset_8h_xhtml"><div class="ttname"><a href="_y_o_l_o_v2_convolution_layer_dataset_8h.xhtml">YOLOV2ConvolutionLayerDataset.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="classarm__compute_1_1test_1_1_convolution_layer_fixture_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">arm_compute::test::ConvolutionLayerFixture</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_convolution_layer_fixture_8h_source.xhtml#l00039">ConvolutionLayerFixture.h:39</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#l00160">ContainerDataset.h:160</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </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="_c_l_accessor_8h_xhtml"><div class="ttname"><a href="_c_l_accessor_8h.xhtml">CLAccessor.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="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_1_1test_1_1benchmark_xhtml_a834ff3152a662fff46917ed60519aaae"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a834ff3152a662fff46917ed60519aaae">arm_compute::test::benchmark::CLGEMMConvolutionLayerFixture</a></div><div class="ttdeci">ConvolutionLayerFixture< CLTensor, CLGEMMConvolutionLayer, CLAccessor > CLGEMMConvolutionLayerFixture</div><div class="ttdef"><b>Definition:</b> <a href="benchmark_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00056">ConvolutionLayer.cpp:56</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_y_o_l_o_v2_convolution_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_convolution_layer_dataset.xhtml">arm_compute::test::datasets::YOLOV2ConvolutionLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_y_o_l_o_v2_convolution_layer_dataset_8h_source.xhtml#l00040">YOLOV2ConvolutionLayerDataset.h:40</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="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_convolution_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_convolution_layer_dataset.xhtml">arm_compute::test::datasets::GoogLeNetInceptionV1ConvolutionLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_goog_le_net_inception_v1_convolution_layer_dataset_8h_source.xhtml#l00068">GoogLeNetInceptionV1ConvolutionLayerDataset.h:68</a></div></div>
<div class="ttc" id="_c_l_tensor_allocator_8h_xhtml"><div class="ttname"><a href="_c_l_tensor_allocator_8h.xhtml">CLTensorAllocator.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="_c_l_g_e_m_m_convolution_layer_8h_xhtml"><div class="ttname"><a href="_c_l_g_e_m_m_convolution_layer_8h.xhtml">CLGEMMConvolutionLayer.h</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="_goog_le_net_inception_v1_convolution_layer_dataset_8h_xhtml"><div class="ttname"><a href="_goog_le_net_inception_v1_convolution_layer_dataset_8h.xhtml">GoogLeNetInceptionV1ConvolutionLayerDataset.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="_le_net5_convolution_layer_dataset_8h_xhtml"><div class="ttname"><a href="_le_net5_convolution_layer_dataset_8h.xhtml">LeNet5ConvolutionLayerDataset.h</a></div></div>
+<div class="ttc" id="utils_2_type_printer_8h_xhtml"><div class="ttname"><a href="utils_2_type_printer_8h.xhtml">TypePrinter.h</a></div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1_c_l_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">arm_compute::test::CLAccessor</a></div><div class="ttdoc">Accessor implementation for CLTensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_c_l_accessor_8h_source.xhtml#l00035">CLAccessor.h:35</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1benchmark_xhtml_a544fd26c293d1af74453a3957473072c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a544fd26c293d1af74453a3957473072c">arm_compute::test::benchmark::REGISTER_FIXTURE_DATA_TEST_CASE</a></div><div class="ttdeci">REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::MobileNetBatchNormalizationLayerDataset(), combine(framework::dataset::make("UseGamma",{false, true}), framework::dataset::make("UseBeta",{false, true}))), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), data_types), framework::dataset::make("DataLayout",{DataLayout::NCHW, DataLayout::NHWC})), framework::dataset::make("Batches", 1)))</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="_squeeze_net_convolution_layer_dataset_8h_xhtml"><div class="ttname"><a href="_squeeze_net_convolution_layer_dataset_8h.xhtml">SqueezeNetConvolutionLayerDataset.h</a></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#l00180">CartesianProductDataset.h:180</a></div></div>
+<div class="ttc" id="_c_l_winograd_convolution_layer_8h_xhtml"><div class="ttname"><a href="_c_l_winograd_convolution_layer_8h.xhtml">CLWinogradConvolutionLayer.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1benchmark_xhtml_a5d95b818519b5889e077646206bf30f8"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1benchmark.xhtml#a5d95b818519b5889e077646206bf30f8">arm_compute::test::benchmark::CLWinogradLayerFixture</a></div><div class="ttdeci">WinogradConvolutionLayerFixture< CLTensor, CLWinogradConvolutionLayer, CLAccessor > CLWinogradLayerFixture</div><div class="ttdef"><b>Definition:</b> <a href="benchmark_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00060">ConvolutionLayer.cpp:60</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="benchmark_2fixtures_2_convolution_layer_fixture_8h_xhtml"><div class="ttname"><a href="benchmark_2fixtures_2_convolution_layer_fixture_8h.xhtml">ConvolutionLayerFixture.h</a></div></div>
-<div class="ttc" id="_goog_le_net_inception_v4_convolution_layer_dataset_8h_xhtml"><div class="ttname"><a href="_goog_le_net_inception_v4_convolution_layer_dataset_8h.xhtml">GoogLeNetInceptionV4ConvolutionLayerDataset.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_le_net5_convolution_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_convolution_layer_dataset.xhtml">arm_compute::test::datasets::LeNet5ConvolutionLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_convolution_layer_dataset_8h_source.xhtml#l00040">LeNet5ConvolutionLayerDataset.h:40</a></div></div>
-<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_convolution_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_convolution_layer_dataset.xhtml">arm_compute::test::datasets::VGG16ConvolutionLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_v_g_g16_convolution_layer_dataset_8h_source.xhtml#l00040">VGG16ConvolutionLayerDataset.h:40</a></div></div>
-<div class="ttc" id="_alex_net_convolution_layer_dataset_8h_xhtml"><div class="ttname"><a href="_alex_net_convolution_layer_dataset_8h.xhtml">AlexNetConvolutionLayerDataset.h</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_c_l_winograd_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">arm_compute::CLWinogradConvolutionLayer</a></div><div class="ttdoc">Basic function to execute Winograd-based convolution on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_convolution_layer_8h_source.xhtml#l00047">CLWinogradConvolutionLayer.h:47</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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00072">Types.h:72</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>
+<div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml">arm_compute::CLTensor</a></div><div class="ttdoc">Basic implementation of the OpenCL tensor interface. </div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8h_source.xhtml#l00039">CLTensor.h:39</a></div></div>
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