arm_compute v17.09
Change-Id: I4bf8f4e6e5f84ce0d5b6f5ba570d276879f42a81
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index 8677afd..9e07bae 100644
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<div id="projectname">Compute Library
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacearm__compute_1_1test_1_1benchmark"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test_1_1benchmark.xhtml">benchmark</a></td></tr>
+<tr class="memitem:namespacearm__compute_1_1test_1_1_c_l_suite"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test_1_1_c_l_suite.xhtml">CLSuite</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacearm__compute_1_1test_1_1cl"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test_1_1cl.xhtml">cl</a></td></tr>
+<tr class="memitem:namespacearm__compute_1_1test_1_1datasets"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test_1_1datasets.xhtml">datasets</a></td></tr>
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-<tr class="memitem:namespacearm__compute_1_1test_1_1cpp11"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test_1_1cpp11.xhtml">cpp11</a></td></tr>
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-<tr class="memitem:namespacearm__compute_1_1test_1_1neon"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test_1_1neon.xhtml">neon</a></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Data Structures</h2></td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_activation_functions.xhtml">ActivationFunctions</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all possible activation functions. <a href="classarm__compute_1_1test_1_1_activation_functions.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_assets_library.xhtml">AssetsLibrary</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Factory class to create and fill tensors. <a href="classarm__compute_1_1test_1_1_assets_library.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_activation_layer_data_object.xhtml">ActivationLayerDataObject</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">ActivationLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_alex_net_activation_layer_dataset.xhtml">AlexNetActivationLayerDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_alex_net_fixture.xhtml">AlexNetFixture</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_alex_net_convolution_layer_dataset.xhtml">AlexNetConvolutionLayerDataset</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">AlexNet's convolution layers tensor shapes. <a href="classarm__compute_1_1test_1_1_alex_net_convolution_layer_dataset.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_fixture.xhtml">BatchNormalizationLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_batch_normalization_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_alex_net_fully_connected_layer_dataset.xhtml">AlexNetFullyConnectedLayerDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">ConvolutionLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_alex_net_normalization_layer_dataset.xhtml">AlexNetNormalizationLayerDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_depthwise_convolution_fixture.xhtml">DepthwiseConvolutionFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_depthwise_convolution_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_alex_net_pooling_layer_dataset.xhtml">AlexNetPoolingLayerDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_depthwise_separable_convolution_layer_fixture.xhtml">DepthwiseSeparableConvolutionLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_depthwise_separable_convolution_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_all_data_types.xhtml">AllDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all data types. <a href="classarm__compute_1_1test_1_1_all_data_types.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_floor_fixture.xhtml">FloorFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_floor_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_data_object.xhtml">BatchNormalizationLayerDataObject</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_fixture.xhtml">FullyConnectedLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_fully_connected_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_border_modes.xhtml">BorderModes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all possible border modes. <a href="classarm__compute_1_1test_1_1_border_modes.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_g_e_m_m_fixture.xhtml">GEMMFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_g_e_m_m_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_c_n_n_data_types.xhtml">CNNDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Supported CNN types. <a href="classarm__compute_1_1test_1_1_c_n_n_data_types.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_le_net5_fixture.xhtml">LeNet5Fixture</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_c_n_n_fixed_point_data_types.xhtml">CNNFixedPointDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Supported CNN fixed point types. <a href="classarm__compute_1_1test_1_1_c_n_n_fixed_point_data_types.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_fixture.xhtml">NormalizationLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_normalization_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_c_n_n_float_data_types.xhtml">CNNFloatDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Supported CNN float types. <a href="classarm__compute_1_1test_1_1_c_n_n_float_data_types.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_fixture.xhtml">PoolingLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_pooling_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structarm__compute_1_1test_1_1common__promoted__signed__type.xhtml">common_promoted_signed_type</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Find the signed promoted common type. <a href="structarm__compute_1_1test_1_1common__promoted__signed__type.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_r_o_i_pooling_layer_fixture.xhtml">ROIPoolingLayerFixture</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Fixture that can be used for NEON and CL. <a href="classarm__compute_1_1test_1_1_r_o_i_pooling_layer_fixture.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_convert_policies.xhtml">ConvertPolicies</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all possible convert/overflow policies. <a href="classarm__compute_1_1test_1_1_convert_policies.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml" title="Accessor implementation for Tensor objects. ">Accessor</a> implementation for <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a> objects. <a href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_data_object.xhtml">ConvolutionLayerDataObject</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Convolution Layer data object. <a href="classarm__compute_1_1test_1_1_convolution_layer_data_object.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_c_l_array_accessor.xhtml">CLArrayAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml" title="Accessor implementation for Tensor objects. ">Accessor</a> implementation for <a class="el" href="classarm__compute_1_1_c_l_array.xhtml">CLArray</a> objects. <a href="classarm__compute_1_1test_1_1_c_l_array_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_data_types.xhtml">DataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Abstract data set containing data types. <a href="classarm__compute_1_1test_1_1_data_types.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_direct_convolution_shapes.xhtml">DirectConvolutionShapes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing direct convolution tensor shapes. <a href="classarm__compute_1_1test_1_1_direct_convolution_shapes.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_fixed_point_data_types.xhtml">FixedPointDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all fixed point data types. <a href="classarm__compute_1_1test_1_1_fixed_point_data_types.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_float_data_types.xhtml">FloatDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all floating point data types. <a href="classarm__compute_1_1test_1_1_float_data_types.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_data_object.xhtml">FullyConnectedLayerDataObject</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_g_e_m_m_data_object.xhtml">GEMMDataObject</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Abstract data set containing multiple objects T. <a href="classarm__compute_1_1test_1_1_generic_dataset.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_activation_layer_dataset.xhtml">GoogLeNetActivationLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_convolution_layer_dataset1.xhtml">GoogLeNetConvolutionLayerDataset1</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">GoogleLeNet v1 convolution layers tensor shapes (Part 1). <a href="classarm__compute_1_1test_1_1_goog_le_net_convolution_layer_dataset1.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_convolution_layer_dataset2.xhtml">GoogLeNetConvolutionLayerDataset2</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">GoogleLeNet v1 convolution layers tensor shapes (Part 2). <a href="classarm__compute_1_1test_1_1_goog_le_net_convolution_layer_dataset2.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_fully_connected_layer_dataset.xhtml">GoogLeNetFullyConnectedLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_g_e_m_m_dataset1.xhtml">GoogLeNetGEMMDataset1</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_g_e_m_m_dataset2.xhtml">GoogLeNetGEMMDataset2</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_normalization_layer_dataset.xhtml">GoogLeNetNormalizationLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_goog_le_net_pooling_layer_dataset.xhtml">GoogLeNetPoolingLayerDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_c_l_lut_accessor.xhtml">CLLutAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml" title="Accessor implementation for Tensor objects. ">Accessor</a> implementation for <a class="el" href="classarm__compute_1_1_c_l_lut.xhtml">CLLut</a> objects. <a href="classarm__compute_1_1test_1_1_c_l_lut_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_i_accessor.xhtml">IAccessor</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Common interface to provide information and access to tensor like structures. <a href="classarm__compute_1_1test_1_1_i_accessor.xhtml#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Common interface to provide information and access to tensor like structures. <a href="classarm__compute_1_1test_1_1_i_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_image_dataset.xhtml">ImageDataset</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Abstract data set containing image names. <a href="classarm__compute_1_1test_1_1_image_dataset.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_i_array_accessor.xhtml">IArrayAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Common interface to provide information and access to array like structures. <a href="classarm__compute_1_1test_1_1_i_array_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_interpolation_policies.xhtml">InterpolationPolicies</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all possible interpolation policies. <a href="classarm__compute_1_1test_1_1_interpolation_policies.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_i_lut_accessor.xhtml">ILutAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Common interface to provide information and access to <a class="el" href="classarm__compute_1_1_lut.xhtml" title="Basic implementation of the LUT interface. ">Lut</a> like structures. <a href="classarm__compute_1_1test_1_1_i_lut_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_large_fully_connected_layer_dataset.xhtml">LargeFullyConnectedLayerDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml" title="Accessor implementation for Tensor objects. ">Accessor</a> implementation for <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a> objects. <a href="classarm__compute_1_1test_1_1_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_large_g_e_m_m_dataset.xhtml">LargeGEMMDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_array_accessor.xhtml">ArrayAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classarm__compute_1_1test_1_1_array_accessor.xhtml" title="ArrayAccessor implementation for Array objects. ">ArrayAccessor</a> implementation for <a class="el" href="classarm__compute_1_1_array.xhtml">Array</a> objects. <a href="classarm__compute_1_1test_1_1_array_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_large_images.xhtml">LargeImages</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing names of large images. <a href="classarm__compute_1_1test_1_1_large_images.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_lut_accessor.xhtml">LutAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml" title="Accessor implementation for Tensor objects. ">Accessor</a> implementation for <a class="el" href="classarm__compute_1_1_lut.xhtml">Lut</a> objects. <a href="classarm__compute_1_1test_1_1_lut_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_large_shapes.xhtml">LargeShapes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing large tensor shapes. <a href="classarm__compute_1_1test_1_1_large_shapes.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_padding_calculator.xhtml">PaddingCalculator</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Calculate required padding. <a href="classarm__compute_1_1test_1_1_padding_calculator.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_le_net5_activation_layer_dataset.xhtml">LeNet5ActivationLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_le_net5_convolution_layer_dataset.xhtml">LeNet5ConvolutionLayerDataset</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">LeNet5's convolution layers tensor shapes. <a href="classarm__compute_1_1test_1_1_le_net5_convolution_layer_dataset.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_le_net5_fully_connected_layer_dataset.xhtml">LeNet5FullyConnectedLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_le_net5_pooling_layer_dataset.xhtml">LeNet5PoolingLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_data_object.xhtml">NormalizationLayerDataObject</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_normalization_types.xhtml">NormalizationTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all possible normalization types. <a href="classarm__compute_1_1test_1_1_normalization_types.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_data_object.xhtml">PoolingLayerDataObject</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_program_options.xhtml">ProgramOptions</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Defines available commandline arguments and allows to parse them. <a href="classarm__compute_1_1test_1_1_program_options.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_random_batch_normalization_layer_dataset.xhtml">RandomBatchNormalizationLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_random_pooling_layer_dataset.xhtml">RandomPoolingLayerDataset</a></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_raw_lut_accessor.xhtml">RawLutAccessor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml" title="Accessor implementation for Tensor objects. ">Accessor</a> implementation for std::map-lut objects. <a href="classarm__compute_1_1test_1_1_raw_lut_accessor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Simple tensor object that stores elements in a consecutive chunk of memory. <a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Subclass of <a class="el" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml" title="Simple tensor object that stores elements in a consecutive chunk of memory. ">SimpleTensor</a> using uint8_t as value type. <a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_rounding_policies.xhtml">RoundingPolicies</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all possible rounding policies. <a href="classarm__compute_1_1test_1_1_rounding_policies.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_shape_dataset.xhtml">ShapeDataset</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Abstract data set containing tensor shapes. <a href="classarm__compute_1_1test_1_1_shape_dataset.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_signed_data_types.xhtml">SignedDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all signed data types. <a href="classarm__compute_1_1test_1_1_signed_data_types.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_small1_d_shape.xhtml">Small1DShape</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing one 1D tensor shape. <a href="classarm__compute_1_1test_1_1_small1_d_shape.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_small_convolution_layer_dataset.xhtml">SmallConvolutionLayerDataset</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing small convolution layer shapes. <a href="classarm__compute_1_1test_1_1_small_convolution_layer_dataset.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_small_fully_connected_layer_dataset.xhtml">SmallFullyConnectedLayerDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_small_g_e_m_m_dataset.xhtml">SmallGEMMDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_small_images.xhtml">SmallImages</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing names of small images. <a href="classarm__compute_1_1test_1_1_small_images.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_small_shapes.xhtml">SmallShapes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing small tensor shapes. <a href="classarm__compute_1_1test_1_1_small_shapes.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Simple tensor object that stores elements in a consecutive chunk of memory. <a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml">TensorCache</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Stores <a class="el" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> categorised by the image they are created from including name, format and channel. <a href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Stores <a class="el" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> categorised by the image they are created from including name, format and channel. <a href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_tensor_library.xhtml">TensorLibrary</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Factory class to create and fill tensors. <a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_threshold_data_object.xhtml">ThresholdDataObject</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_threshold_dataset.xhtml">ThresholdDataset</a></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1_unsigned_data_types.xhtml">UnsignedDataTypes</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Data set containing all unsigned data types. <a href="classarm__compute_1_1test_1_1_unsigned_data_types.xhtml#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structarm__compute_1_1test_1_1_user_configuration.xhtml">UserConfiguration</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Container providing easy access to runtime options provided by the user. <a href="structarm__compute_1_1test_1_1_user_configuration.xhtml#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structarm__compute_1_1test_1_1common__promoted__signed__type.xhtml">common_promoted_signed_type</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Find the signed promoted common type. <a href="structarm__compute_1_1test_1_1common__promoted__signed__type.xhtml#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
-<tr class="memitem:aa1a629d971f45dc8c4cb7ec2d5c8728e"><td class="memTemplParams" colspan="2">template<unsigned int Size> </td></tr>
-<tr class="memitem:aa1a629d971f45dc8c4cb7ec2d5c8728e"><td class="memTemplItemLeft" align="right" valign="top">using </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa1a629d971f45dc8c4cb7ec2d5c8728e">ActivationLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a>< <a class="el" href="classarm__compute_1_1test_1_1_activation_layer_data_object.xhtml">ActivationLayerDataObject</a>, Size ></td></tr>
-<tr class="separator:aa1a629d971f45dc8c4cb7ec2d5c8728e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6b006671b13de04edb1001f32966d9de"><td class="memTemplParams" colspan="2">template<unsigned int Size> </td></tr>
-<tr class="memitem:a6b006671b13de04edb1001f32966d9de"><td class="memTemplItemLeft" align="right" valign="top">using </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6b006671b13de04edb1001f32966d9de">BatchNormalizationLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a>< <a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_data_object.xhtml">BatchNormalizationLayerDataObject</a>, Size ></td></tr>
-<tr class="separator:a6b006671b13de04edb1001f32966d9de"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a18ed03ad3383da62aec512f04372d2f7"><td class="memTemplParams" colspan="2">template<unsigned int Size> </td></tr>
-<tr class="memitem:a18ed03ad3383da62aec512f04372d2f7"><td class="memTemplItemLeft" align="right" valign="top">using </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a18ed03ad3383da62aec512f04372d2f7">ConvolutionLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a>< <a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_data_object.xhtml">ConvolutionLayerDataObject</a>, Size ></td></tr>
-<tr class="separator:a18ed03ad3383da62aec512f04372d2f7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad41fe013a10729c8790f3d30bfeac8f1"><td class="memTemplParams" colspan="2">template<unsigned int Size> </td></tr>
-<tr class="memitem:ad41fe013a10729c8790f3d30bfeac8f1"><td class="memTemplItemLeft" align="right" valign="top">using </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad41fe013a10729c8790f3d30bfeac8f1">FullyConnectedLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a>< <a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_data_object.xhtml">FullyConnectedLayerDataObject</a>, Size ></td></tr>
-<tr class="separator:ad41fe013a10729c8790f3d30bfeac8f1"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a62dd75dba5587b539e722ea857347c26"><td class="memTemplParams" colspan="2">template<unsigned int Size> </td></tr>
-<tr class="memitem:a62dd75dba5587b539e722ea857347c26"><td class="memTemplItemLeft" align="right" valign="top">using </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a62dd75dba5587b539e722ea857347c26">NormalizationLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a>< <a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_data_object.xhtml">NormalizationLayerDataObject</a>, Size ></td></tr>
-<tr class="separator:a62dd75dba5587b539e722ea857347c26"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:af11de09f727dca1d78602572247fa5ff"><td class="memTemplParams" colspan="2">template<unsigned int Size> </td></tr>
-<tr class="memitem:af11de09f727dca1d78602572247fa5ff"><td class="memTemplItemLeft" align="right" valign="top">using </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af11de09f727dca1d78602572247fa5ff">PoolingLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a>< <a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_data_object.xhtml">PoolingLayerDataObject</a>, Size ></td></tr>
-<tr class="separator:af11de09f727dca1d78602572247fa5ff"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a74a10374253178ae54e1baab173698a1"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a74a10374253178ae54e1baab173698a1">CLActivationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">ActivationLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">CLActivationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:a74a10374253178ae54e1baab173698a1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af80ea91532f0ebdccb3f1d8e507a98ad"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af80ea91532f0ebdccb3f1d8e507a98ad">CLBatchNormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_fixture.xhtml">BatchNormalizationLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_batch_normalization_layer.xhtml">CLBatchNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:af80ea91532f0ebdccb3f1d8e507a98ad"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad275d75e1b63f91fdc59afe026688b12"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">ConvolutionLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_convolution_layer.xhtml">CLConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:ad275d75e1b63f91fdc59afe026688b12"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1e3870d2e47dfd84b259bdbff0a6f5f8"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1e3870d2e47dfd84b259bdbff0a6f5f8">CLDepthwiseConvolutionFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_depthwise_convolution_fixture.xhtml">DepthwiseConvolutionFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_depthwise_convolution.xhtml">CLDepthwiseConvolution</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:a1e3870d2e47dfd84b259bdbff0a6f5f8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:adc07e82b4049d653c965af2606a7d70f"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#adc07e82b4049d653c965af2606a7d70f">CLDepthwiseSeparableConvolutionLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_depthwise_separable_convolution_layer_fixture.xhtml">DepthwiseSeparableConvolutionLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_depthwise_separable_convolution_layer.xhtml">CLDepthwiseSeparableConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:adc07e82b4049d653c965af2606a7d70f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4a14e383a632057e99845c74a72a6454"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4a14e383a632057e99845c74a72a6454">CLFloorFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_floor_fixture.xhtml">FloorFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_floor.xhtml">CLFloor</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:a4a14e383a632057e99845c74a72a6454"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4c33955ce3f6ed3a4d756cdebf6c8b3a"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_fixture.xhtml">FullyConnectedLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:a4c33955ce3f6ed3a4d756cdebf6c8b3a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abf07c2bf7d8e9c76e146f9b21bee88fd"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#abf07c2bf7d8e9c76e146f9b21bee88fd">CLGEMMFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_g_e_m_m_fixture.xhtml">GEMMFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml">CLGEMM</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:abf07c2bf7d8e9c76e146f9b21bee88fd"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af4f1c6ad288931f07f614316f57ed63b"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af4f1c6ad288931f07f614316f57ed63b">CLNormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_fixture.xhtml">NormalizationLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_normalization_layer.xhtml">CLNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:af4f1c6ad288931f07f614316f57ed63b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9c81648f3199d0d1c3f34a29a7a2bb8d"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_fixture.xhtml">PoolingLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_pooling_layer.xhtml">CLPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> ></td></tr>
+<tr class="separator:a9c81648f3199d0d1c3f34a29a7a2bb8d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a41884dec2ecae6674396802641b01060"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a41884dec2ecae6674396802641b01060">CLROIPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_r_o_i_pooling_layer_fixture.xhtml">ROIPoolingLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_r_o_i_pooling_layer.xhtml">CLROIPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>, <a class="el" href="classarm__compute_1_1_c_l_array.xhtml">CLArray</a>< <a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a> >, <a class="el" href="classarm__compute_1_1test_1_1_c_l_array_accessor.xhtml">CLArrayAccessor</a>< <a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a> >></td></tr>
+<tr class="separator:a41884dec2ecae6674396802641b01060"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa631c5ec3d7cb3dab649f994e9e9217d"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa631c5ec3d7cb3dab649f994e9e9217d">CLAlexNetFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_alex_net_fixture.xhtml">AlexNetFixture</a>< <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_sub_tensor.xhtml">CLSubTensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>, <a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">CLActivationLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_convolution_layer.xhtml">CLConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_direct_convolution_layer.xhtml">CLDirectConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_normalization_layer.xhtml">CLNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_pooling_layer.xhtml">CLPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_softmax_layer.xhtml">CLSoftmaxLayer</a> ></td></tr>
+<tr class="separator:aa631c5ec3d7cb3dab649f994e9e9217d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae3b678c8477dd5acc5e264eae37b562c"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae3b678c8477dd5acc5e264eae37b562c">CLLeNet5Fixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_le_net5_fixture.xhtml">LeNet5Fixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>, <a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">CLActivationLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_convolution_layer.xhtml">CLConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_pooling_layer.xhtml">CLPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_softmax_layer.xhtml">CLSoftmaxLayer</a> ></td></tr>
+<tr class="separator:ae3b678c8477dd5acc5e264eae37b562c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aeded391cb7ec7a44c41eb23544265894"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">ActivationLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:aeded391cb7ec7a44c41eb23544265894"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac7369c169e6de526fcb6f68e4a959444"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac7369c169e6de526fcb6f68e4a959444">NEBatchNormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_fixture.xhtml">BatchNormalizationLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_batch_normalization_layer.xhtml">NEBatchNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:ac7369c169e6de526fcb6f68e4a959444"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3168ad22b6ac1e9a6996b53e5038a7a2"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">ConvolutionLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">NEConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:a3168ad22b6ac1e9a6996b53e5038a7a2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac8cf6873b0e9ac7334bcbc042fdc5f02"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac8cf6873b0e9ac7334bcbc042fdc5f02">NEFloorFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_floor_fixture.xhtml">FloorFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_floor.xhtml">NEFloor</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:ac8cf6873b0e9ac7334bcbc042fdc5f02"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0b4f7a523ddb2b823750ff5bdc03470c"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_fixture.xhtml">FullyConnectedLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:a0b4f7a523ddb2b823750ff5bdc03470c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a789c444c1307e85eec5f8b0d75fd5f7d"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a789c444c1307e85eec5f8b0d75fd5f7d">NEGEMMFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_g_e_m_m_fixture.xhtml">GEMMFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:a789c444c1307e85eec5f8b0d75fd5f7d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acc2c4764a300b505b50e9ba0642eff2b"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#acc2c4764a300b505b50e9ba0642eff2b">NENormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_fixture.xhtml">NormalizationLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_normalization_layer.xhtml">NENormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:acc2c4764a300b505b50e9ba0642eff2b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aafcc5ee5a13d9ed18d31591bb1d50fb0"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_fixture.xhtml">PoolingLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_pooling_layer.xhtml">NEPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> ></td></tr>
+<tr class="separator:aafcc5ee5a13d9ed18d31591bb1d50fb0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7ad74154ac625702bef70b90243ae63f"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a7ad74154ac625702bef70b90243ae63f">NEROIPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_r_o_i_pooling_layer_fixture.xhtml">ROIPoolingLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_r_o_i_pooling_layer.xhtml">NEROIPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>, <a class="el" href="classarm__compute_1_1_array.xhtml">Array</a>< <a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a> >, <a class="el" href="classarm__compute_1_1test_1_1_array_accessor.xhtml">ArrayAccessor</a>< <a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a> >></td></tr>
+<tr class="separator:a7ad74154ac625702bef70b90243ae63f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae0e8bcf3b0ed15e708b4a38febfdb84e"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae0e8bcf3b0ed15e708b4a38febfdb84e">NEAlexNetFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_alex_net_fixture.xhtml">AlexNetFixture</a>< <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a>, <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_sub_tensor.xhtml">SubTensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>, <a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">NEConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">NEConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_normalization_layer.xhtml">NENormalizationLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_pooling_layer.xhtml">NEPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml">NESoftmaxLayer</a> ></td></tr>
+<tr class="separator:ae0e8bcf3b0ed15e708b4a38febfdb84e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6a292ad5fedcc7dea6c6eb1be6d4c0d3"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6a292ad5fedcc7dea6c6eb1be6d4c0d3">NELeNet5Fixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_le_net5_fixture.xhtml">LeNet5Fixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>, <a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">NEConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_pooling_layer.xhtml">NEPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml">NESoftmaxLayer</a> ></td></tr>
+<tr class="separator:a6a292ad5fedcc7dea6c6eb1be6d4c0d3"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
-<tr class="memitem:ac04d36ea1fb41a3dfd3afd4a89ef2470"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">swap</a> (<a class="el" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &tensor1, <a class="el" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &tensor2)</td></tr>
-<tr class="separator:ac04d36ea1fb41a3dfd3afd4a89ef2470"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2fba44656470195a6245f922a1c264f5"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a2fba44656470195a6245f922a1c264f5">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af80ea91532f0ebdccb3f1d8e507a98ad">CLBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_batch_normalization_layer_dataset.xhtml">datasets::YOLOV2BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a2fba44656470195a6245f922a1c264f5"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a365d9325d2bba7fbf9983f80bfe8c796"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a365d9325d2bba7fbf9983f80bfe8c796">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af80ea91532f0ebdccb3f1d8e507a98ad">CLBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_batch_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a365d9325d2bba7fbf9983f80bfe8c796"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2abd12df8c0c36eed83982ee073db2ff"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a2abd12df8c0c36eed83982ee073db2ff">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af80ea91532f0ebdccb3f1d8e507a98ad">CLBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_batch_normalization_layer_dataset.xhtml">datasets::YOLOV2BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a2abd12df8c0c36eed83982ee073db2ff"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8457ac77df7a142e86354ac08fd5ba30"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a8457ac77df7a142e86354ac08fd5ba30">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af80ea91532f0ebdccb3f1d8e507a98ad">CLBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_batch_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a8457ac77df7a142e86354ac08fd5ba30"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a80d1181d85aefe33d6a8720152dba80b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a80d1181d85aefe33d6a8720152dba80b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_convolution_layer_dataset.xhtml">datasets::AlexNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a80d1181d85aefe33d6a8720152dba80b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac18e51057a61f7db456d1c8b9b03aa09"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac18e51057a61f7db456d1c8b9b03aa09">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_convolution_layer_dataset.xhtml">datasets::LeNet5ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ac18e51057a61f7db456d1c8b9b03aa09"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3e452462cb397897f476f6d83b468914"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a3e452462cb397897f476f6d83b468914">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a3e452462cb397897f476f6d83b468914"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aecd85eec5df288174be9b7e0fac6d1fe"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aecd85eec5df288174be9b7e0fac6d1fe">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:aecd85eec5df288174be9b7e0fac6d1fe"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aea25c6951a5d4bcfeb95750105154506"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aea25c6951a5d4bcfeb95750105154506">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:aea25c6951a5d4bcfeb95750105154506"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac62a5389a4a60e89fabb6bb2153adfc5"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac62a5389a4a60e89fabb6bb2153adfc5">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_convolution_layer_dataset.xhtml">datasets::AlexNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ac62a5389a4a60e89fabb6bb2153adfc5"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a65a028ab7f8ba81db43d5963ea5343a4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a65a028ab7f8ba81db43d5963ea5343a4">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_convolution_layer_dataset.xhtml">datasets::LeNet5ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a65a028ab7f8ba81db43d5963ea5343a4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6773bc983eece85b34d67c4ba3c09554"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6773bc983eece85b34d67c4ba3c09554">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a6773bc983eece85b34d67c4ba3c09554"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4bdfdac4318cf7e4b09cc13a553363a7"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4bdfdac4318cf7e4b09cc13a553363a7">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a4bdfdac4318cf7e4b09cc13a553363a7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a34df6fb97233366fc9083d79c13a5737"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a34df6fb97233366fc9083d79c13a5737">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a34df6fb97233366fc9083d79c13a5737"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5a371e1a37be130dc9e8c905cd5efc29"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a5a371e1a37be130dc9e8c905cd5efc29">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_convolution_layer_dataset.xhtml">datasets::VGG16ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4})))</td></tr>
+<tr class="separator:a5a371e1a37be130dc9e8c905cd5efc29"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7473924d4fdf2b5dec0d8ee9aa11e25d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a7473924d4fdf2b5dec0d8ee9aa11e25d">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_convolution_layer_dataset.xhtml">datasets::YOLOV2ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:a7473924d4fdf2b5dec0d8ee9aa11e25d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad7d919409d3d679cfbf28b2dae757fec"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad7d919409d3d679cfbf28b2dae757fec">REGISTER_FIXTURE_DATA_TEST_CASE</a> (MobileNetDepthwiseConvolution, <a class="el" href="namespacearm__compute_1_1test.xhtml#a1e3870d2e47dfd84b259bdbff0a6f5f8">CLDepthwiseConvolutionFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_mobile_net_depthwise_convolution_dataset.xhtml">datasets::MobileNetDepthwiseConvolutionDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1})))</td></tr>
+<tr class="separator:ad7d919409d3d679cfbf28b2dae757fec"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1f4b9eae17da2aebc223b0fdeee74cea"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1f4b9eae17da2aebc223b0fdeee74cea">REGISTER_FIXTURE_DATA_TEST_CASE</a> (MobileNetDepthwiseSeparableConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#adc07e82b4049d653c965af2606a7d70f">CLDepthwiseSeparableConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_mobile_net_depthwise_separable_convolution_layer_dataset.xhtml">datasets::MobileNetDepthwiseSeparableConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1})))</td></tr>
+<tr class="separator:a1f4b9eae17da2aebc223b0fdeee74cea"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad52c9735c67d5972016f143cd15ea874"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad52c9735c67d5972016f143cd15ea874">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_direct_convolution_layer_dataset.xhtml">datasets::AlexNetDirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ad52c9735c67d5972016f143cd15ea874"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa971f54dfef950c44d8973db82b91e4e"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa971f54dfef950c44d8973db82b91e4e">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:aa971f54dfef950c44d8973db82b91e4e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa0a358cbff96894b77c9b3cfba3c2db4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa0a358cbff96894b77c9b3cfba3c2db4">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:aa0a358cbff96894b77c9b3cfba3c2db4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:afd1574f92e2d5a179c3c7f0e8e438bba"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#afd1574f92e2d5a179c3c7f0e8e438bba">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:afd1574f92e2d5a179c3c7f0e8e438bba"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a91a53a55f1c814837ea5374d1c2095e8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a91a53a55f1c814837ea5374d1c2095e8">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_direct_convolution_layer_dataset.xhtml">datasets::AlexNetDirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a91a53a55f1c814837ea5374d1c2095e8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a959373f15eeea41ce740f0bc0ce2244a"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a959373f15eeea41ce740f0bc0ce2244a">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a959373f15eeea41ce740f0bc0ce2244a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abb38304d29f99717ecc5c528962972a5"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#abb38304d29f99717ecc5c528962972a5">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:abb38304d29f99717ecc5c528962972a5"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae88c882e06dad040d2bb6278ef8c4c84"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae88c882e06dad040d2bb6278ef8c4c84">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ae88c882e06dad040d2bb6278ef8c4c84"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab0595cda883cec6b1b3a5389fd786e9f"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ab0595cda883cec6b1b3a5389fd786e9f">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_convolution_layer_dataset.xhtml">datasets::VGG16ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:ab0595cda883cec6b1b3a5389fd786e9f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aabdb95f3f541376f38e03d63957cd0af"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aabdb95f3f541376f38e03d63957cd0af">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_convolution_layer_dataset.xhtml">datasets::YOLOV2ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:aabdb95f3f541376f38e03d63957cd0af"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6e81878e7ca8fecdd9f6e6bcc2a7b794"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6e81878e7ca8fecdd9f6e6bcc2a7b794">REGISTER_FIXTURE_DATA_TEST_CASE</a> (Floor, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4a14e383a632057e99845c74a72a6454">CLFloorFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_small_shapes.xhtml">datasets::SmallShapes</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:a6e81878e7ca8fecdd9f6e6bcc2a7b794"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae5411ce056673117b799d20c1c9484dd"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae5411ce056673117b799d20c1c9484dd">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetFullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_fully_connected_layer_dataset.xhtml">datasets::AlexNetFullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ae5411ce056673117b799d20c1c9484dd"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae63877ff99387d51d4abc340a50f1093"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae63877ff99387d51d4abc340a50f1093">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_fully_connected_layer_dataset.xhtml">datasets::LeNet5FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ae63877ff99387d51d4abc340a50f1093"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a49ab3e510552d29b5698d55ef52674c3"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a49ab3e510552d29b5698d55ef52674c3">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_fully_connected_layer_dataset.xhtml">datasets::VGG16FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a49ab3e510552d29b5698d55ef52674c3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8cf1a9e06ce42b8ff57bf13aa2c2c047"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a8cf1a9e06ce42b8ff57bf13aa2c2c047">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a8cf1a9e06ce42b8ff57bf13aa2c2c047"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8388dbd479dceaffb21c8fc564c5c420"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a8388dbd479dceaffb21c8fc564c5c420">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a8388dbd479dceaffb21c8fc564c5c420"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab8549a72a0983f5281a5612979669e2d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ab8549a72a0983f5281a5612979669e2d">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetFullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_fully_connected_layer_dataset.xhtml">datasets::AlexNetFullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ab8549a72a0983f5281a5612979669e2d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:adb9a698039f2f9414f3296a4d9070893"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#adb9a698039f2f9414f3296a4d9070893">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_fully_connected_layer_dataset.xhtml">datasets::LeNet5FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:adb9a698039f2f9414f3296a4d9070893"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6fef2cd462f91f3b071ee7d02322dbb6"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6fef2cd462f91f3b071ee7d02322dbb6">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_fully_connected_layer_dataset.xhtml">datasets::VGG16FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a6fef2cd462f91f3b071ee7d02322dbb6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a463d7f372ea5c6217b8ee151b47f596e"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a463d7f372ea5c6217b8ee151b47f596e">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a463d7f372ea5c6217b8ee151b47f596e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad5d101b25bb35a8d9b1efc70102bb3a4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad5d101b25bb35a8d9b1efc70102bb3a4">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ad5d101b25bb35a8d9b1efc70102bb3a4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2b1950a60a98dec32a7ca7531fded8ad"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a2b1950a60a98dec32a7ca7531fded8ad">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1GEMM, <a class="el" href="namespacearm__compute_1_1test.xhtml#abf07c2bf7d8e9c76e146f9b21bee88fd">CLGEMMFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_g_e_m_m_dataset.xhtml">datasets::GoogLeNetInceptionV1GEMMDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:a2b1950a60a98dec32a7ca7531fded8ad"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aef76c7b3cf7d1e3c6fc7ff96804a5753"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aef76c7b3cf7d1e3c6fc7ff96804a5753">REGISTER_FIXTURE_DATA_TEST_CASE</a> (MatrixMultiplyGEMM, <a class="el" href="namespacearm__compute_1_1test.xhtml#abf07c2bf7d8e9c76e146f9b21bee88fd">CLGEMMFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_matrix_multiply_g_e_m_m_dataset.xhtml">datasets::MatrixMultiplyGEMMDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:aef76c7b3cf7d1e3c6fc7ff96804a5753"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af935c08091163839aead6ac3023c2147"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af935c08091163839aead6ac3023c2147">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogleNetGEMM, <a class="el" href="namespacearm__compute_1_1test.xhtml#abf07c2bf7d8e9c76e146f9b21bee88fd">CLGEMMFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_google_net_g_e_m_m_dataset.xhtml">datasets::GoogleNetGEMMDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:af935c08091163839aead6ac3023c2147"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a70d28ab3b5936a6454451d42f3c170f3"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a70d28ab3b5936a6454451d42f3c170f3">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af4f1c6ad288931f07f614316f57ed63b">CLNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_normalization_layer_dataset.xhtml">datasets::AlexNetNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a70d28ab3b5936a6454451d42f3c170f3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:afc6ba7f0f4b792e2df1270d8f83f138d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#afc6ba7f0f4b792e2df1270d8f83f138d">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1NormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af4f1c6ad288931f07f614316f57ed63b">CLNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1NormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:afc6ba7f0f4b792e2df1270d8f83f138d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae4dd72b2a2e5af0c89c5ce7d2443e115"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae4dd72b2a2e5af0c89c5ce7d2443e115">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af4f1c6ad288931f07f614316f57ed63b">CLNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_normalization_layer_dataset.xhtml">datasets::AlexNetNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ae4dd72b2a2e5af0c89c5ce7d2443e115"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a62af6d63be834c648f251c0497e7b59f"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a62af6d63be834c648f251c0497e7b59f">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1NormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#af4f1c6ad288931f07f614316f57ed63b">CLNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1NormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a62af6d63be834c648f251c0497e7b59f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0e999f1d9f9608da6fb4bbde4afc078e"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a0e999f1d9f9608da6fb4bbde4afc078e">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_pooling_layer_dataset.xhtml">datasets::AlexNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a0e999f1d9f9608da6fb4bbde4afc078e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0889351f9ee837c8009925f17dd4688b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a0889351f9ee837c8009925f17dd4688b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_pooling_layer_dataset.xhtml">datasets::LeNet5PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a0889351f9ee837c8009925f17dd4688b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a903e3acaf54969c5d276058e979a753c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a903e3acaf54969c5d276058e979a753c">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a903e3acaf54969c5d276058e979a753c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a85078031088e419e7f928e5ad5bbafa9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a85078031088e419e7f928e5ad5bbafa9">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a85078031088e419e7f928e5ad5bbafa9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a27cf9c407e83adbf0c837240a6bc3534"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a27cf9c407e83adbf0c837240a6bc3534">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_pooling_layer_dataset.xhtml">datasets::SqueezeNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a27cf9c407e83adbf0c837240a6bc3534"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:afd017468a129b6650b73cb65ecc40516"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#afd017468a129b6650b73cb65ecc40516">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_pooling_layer_dataset.xhtml">datasets::VGG16PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:afd017468a129b6650b73cb65ecc40516"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a36cad137a713f8be3263a1a6466c6bd7"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a36cad137a713f8be3263a1a6466c6bd7">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_pooling_layer_dataset.xhtml">datasets::YOLOV2PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a36cad137a713f8be3263a1a6466c6bd7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4558b577be27af2ceffffd986b1aab7f"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4558b577be27af2ceffffd986b1aab7f">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_pooling_layer_dataset.xhtml">datasets::AlexNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a4558b577be27af2ceffffd986b1aab7f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a731977f1de2e0d6dd1512818540ab608"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a731977f1de2e0d6dd1512818540ab608">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_pooling_layer_dataset.xhtml">datasets::LeNet5PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a731977f1de2e0d6dd1512818540ab608"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a967825a64c529b573ca62e74179ee921"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a967825a64c529b573ca62e74179ee921">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a967825a64c529b573ca62e74179ee921"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac06bd6612edf1bbb0c0f4b0d4aa86b32"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac06bd6612edf1bbb0c0f4b0d4aa86b32">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ac06bd6612edf1bbb0c0f4b0d4aa86b32"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5f97a3f0575116d348f47489487d4214"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a5f97a3f0575116d348f47489487d4214">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_pooling_layer_dataset.xhtml">datasets::SqueezeNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a5f97a3f0575116d348f47489487d4214"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6a51ef57457c994f04d0b54e76387add"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6a51ef57457c994f04d0b54e76387add">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_pooling_layer_dataset.xhtml">datasets::VGG16PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a6a51ef57457c994f04d0b54e76387add"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7d579c9d463693975486ea2248adc966"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a7d579c9d463693975486ea2248adc966">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_pooling_layer_dataset.xhtml">datasets::YOLOV2PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a7d579c9d463693975486ea2248adc966"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac7d54f1a842ebb07f378846c21ccbe97"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac7d54f1a842ebb07f378846c21ccbe97">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SmallROIPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a41884dec2ecae6674396802641b01060">CLROIPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_small_r_o_i_pooling_layer_dataset.xhtml">datasets::SmallROIPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("DataType",{DataType::F16, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>})), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:ac7d54f1a842ebb07f378846c21ccbe97"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a69b2d4f81544c38878bd196d49d41360"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a69b2d4f81544c38878bd196d49d41360">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNet, <a class="el" href="namespacearm__compute_1_1test.xhtml#aa631c5ec3d7cb3dab649f994e9e9217d">CLAlexNetFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("DataType",{DataType::F16, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>}), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:a69b2d4f81544c38878bd196d49d41360"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a485c6b6af55e2f12c1b7ef40546c08f7"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a485c6b6af55e2f12c1b7ef40546c08f7">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5, <a class="el" href="namespacearm__compute_1_1test.xhtml#ae3b678c8477dd5acc5e264eae37b562c">CLLeNet5Fixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8}))</td></tr>
+<tr class="separator:a485c6b6af55e2f12c1b7ef40546c08f7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abfd4fd028574ac46a9d056e7a1ead6f7"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#abfd4fd028574ac46a9d056e7a1ead6f7">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_activation_layer_dataset.xhtml">datasets::AlexNetActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:abfd4fd028574ac46a9d056e7a1ead6f7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a70381b263268259b4b6fbff88a0526c4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a70381b263268259b4b6fbff88a0526c4">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_activation_layer_dataset.xhtml">datasets::LeNet5ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a70381b263268259b4b6fbff88a0526c4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:add697a0c19a1638874c37d5d15fc2d83"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#add697a0c19a1638874c37d5d15fc2d83">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_activation_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:add697a0c19a1638874c37d5d15fc2d83"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a117bc733390c845f7493e6dad0b75191"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a117bc733390c845f7493e6dad0b75191">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_activation_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a117bc733390c845f7493e6dad0b75191"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a70f04129817692e0b5727fb542f9153c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a70f04129817692e0b5727fb542f9153c">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_activation_layer_dataset.xhtml">datasets::SqueezeNetActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a70f04129817692e0b5727fb542f9153c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab10eddd065a1bdb9c6b09cb1e1382f5a"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ab10eddd065a1bdb9c6b09cb1e1382f5a">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_activation_layer_dataset.xhtml">datasets::VGG16ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ab10eddd065a1bdb9c6b09cb1e1382f5a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a572b94c09ce496eda95d8d544dc1c4d1"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a572b94c09ce496eda95d8d544dc1c4d1">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_activation_layer_dataset.xhtml">datasets::YOLOV2ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a572b94c09ce496eda95d8d544dc1c4d1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7677396611ac11166c6f7344f9e0ef12"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a7677396611ac11166c6f7344f9e0ef12">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_activation_layer_dataset.xhtml">datasets::AlexNetActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a7677396611ac11166c6f7344f9e0ef12"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1afabd3008ddf541288b01fe746ab284"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1afabd3008ddf541288b01fe746ab284">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_activation_layer_dataset.xhtml">datasets::LeNet5ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a1afabd3008ddf541288b01fe746ab284"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a164c7e1e8e2e4de0ce6282d2b0835dd0"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a164c7e1e8e2e4de0ce6282d2b0835dd0">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_activation_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a164c7e1e8e2e4de0ce6282d2b0835dd0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6ada452bc1053385b8574f38d341ffc9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6ada452bc1053385b8574f38d341ffc9">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_activation_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a6ada452bc1053385b8574f38d341ffc9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6692a58c12e2eff315715e6c971d0230"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6692a58c12e2eff315715e6c971d0230">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_activation_layer_dataset.xhtml">datasets::SqueezeNetActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a6692a58c12e2eff315715e6c971d0230"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a26e3678291b5f879d82808eda0d39bc2"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a26e3678291b5f879d82808eda0d39bc2">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_activation_layer_dataset.xhtml">datasets::VGG16ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a26e3678291b5f879d82808eda0d39bc2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab77581768cf2f7433ba92c2b42c4617e"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ab77581768cf2f7433ba92c2b42c4617e">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2ActivationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_activation_layer_dataset.xhtml">datasets::YOLOV2ActivationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ab77581768cf2f7433ba92c2b42c4617e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9e4dd8377091a877cf271bb34f2ed7da"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9e4dd8377091a877cf271bb34f2ed7da">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ac7369c169e6de526fcb6f68e4a959444">NEBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_batch_normalization_layer_dataset.xhtml">datasets::YOLOV2BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a9e4dd8377091a877cf271bb34f2ed7da"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a09be4cd69df94f0929598f03b32001f0"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a09be4cd69df94f0929598f03b32001f0">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ac7369c169e6de526fcb6f68e4a959444">NEBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_batch_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a09be4cd69df94f0929598f03b32001f0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa37f90a45822a6f45002ad5fd1e69560"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa37f90a45822a6f45002ad5fd1e69560">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ac7369c169e6de526fcb6f68e4a959444">NEBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_batch_normalization_layer_dataset.xhtml">datasets::YOLOV2BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:aa37f90a45822a6f45002ad5fd1e69560"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa93e94a58a377d2493868e24d746531b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa93e94a58a377d2493868e24d746531b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4BatchNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#ac7369c169e6de526fcb6f68e4a959444">NEBatchNormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_batch_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:aa93e94a58a377d2493868e24d746531b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4fc639ce3410d3609337137d44a68ac9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4fc639ce3410d3609337137d44a68ac9">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_convolution_layer_dataset.xhtml">datasets::AlexNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a4fc639ce3410d3609337137d44a68ac9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7da7dbada8d8e076c78d1402743b7de9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a7da7dbada8d8e076c78d1402743b7de9">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_convolution_layer_dataset.xhtml">datasets::LeNet5ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a7da7dbada8d8e076c78d1402743b7de9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9c30ac20d9eae69db3b004f36d8efaca"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9c30ac20d9eae69db3b004f36d8efaca">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a9c30ac20d9eae69db3b004f36d8efaca"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abe4e6a4ff5c68a5403ec4dc38149d097"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#abe4e6a4ff5c68a5403ec4dc38149d097">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:abe4e6a4ff5c68a5403ec4dc38149d097"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2a91a938df2a246bb92811fe90bf5ee0"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a2a91a938df2a246bb92811fe90bf5ee0">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a2a91a938df2a246bb92811fe90bf5ee0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8c86e43926d24040dbbc73e5ad638dea"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a8c86e43926d24040dbbc73e5ad638dea">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_convolution_layer_dataset.xhtml">datasets::AlexNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a8c86e43926d24040dbbc73e5ad638dea"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a11c4e187683f0687472d48d8f279c8fc"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a11c4e187683f0687472d48d8f279c8fc">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_convolution_layer_dataset.xhtml">datasets::LeNet5ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a11c4e187683f0687472d48d8f279c8fc"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3df552423cbf598c725b9dc615f06315"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a3df552423cbf598c725b9dc615f06315">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a3df552423cbf598c725b9dc615f06315"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4470fc8180788f756fccdb77f9a25886"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4470fc8180788f756fccdb77f9a25886">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a4470fc8180788f756fccdb77f9a25886"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a029d80ad64be335749e827cc64efd88c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a029d80ad64be335749e827cc64efd88c">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a029d80ad64be335749e827cc64efd88c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a68166bcb788035f5a6c17fe0c68ae730"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a68166bcb788035f5a6c17fe0c68ae730">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_convolution_layer_dataset.xhtml">datasets::VGG16ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4})))</td></tr>
+<tr class="separator:a68166bcb788035f5a6c17fe0c68ae730"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0ca04d4de125be45c16b579b43d53835"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a0ca04d4de125be45c16b579b43d53835">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2ConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_convolution_layer_dataset.xhtml">datasets::YOLOV2ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:a0ca04d4de125be45c16b579b43d53835"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abe7167f9af260495f067dd8f36251a3b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#abe7167f9af260495f067dd8f36251a3b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_direct_convolution_layer_dataset.xhtml">datasets::AlexNetDirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:abe7167f9af260495f067dd8f36251a3b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:adc3f7b3f1d06144af1980e8705253583"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#adc3f7b3f1d06144af1980e8705253583">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:adc3f7b3f1d06144af1980e8705253583"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9cc3e01ede750344f389191184d4682d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9cc3e01ede750344f389191184d4682d">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a9cc3e01ede750344f389191184d4682d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9c7a41c764eb85334c2d75df71d40cc4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9c7a41c764eb85334c2d75df71d40cc4">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a9c7a41c764eb85334c2d75df71d40cc4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad8c07298bae2d7cd7ace3ad869371b0b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad8c07298bae2d7cd7ace3ad869371b0b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_direct_convolution_layer_dataset.xhtml">datasets::AlexNetDirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ad8c07298bae2d7cd7ace3ad869371b0b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a981537b01124fe1025ab51dfe0dde1ee"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a981537b01124fe1025ab51dfe0dde1ee">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a981537b01124fe1025ab51dfe0dde1ee"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a27446bd5b343d26d6028cd2ab34065a6"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a27446bd5b343d26d6028cd2ab34065a6">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_direct_convolution_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a27446bd5b343d26d6028cd2ab34065a6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a13170587db62e123a041d2b8cab82ef8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a13170587db62e123a041d2b8cab82ef8">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetDirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_convolution_layer_dataset.xhtml">datasets::SqueezeNetConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a13170587db62e123a041d2b8cab82ef8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1f92978c7363135053baa95b94501676"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1f92978c7363135053baa95b94501676">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_convolution_layer_dataset.xhtml">datasets::VGG16ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:a1f92978c7363135053baa95b94501676"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af3310a6693b1d28b4d474e2a025b8777"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af3310a6693b1d28b4d474e2a025b8777">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2DirectConvolutionLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_convolution_layer_dataset.xhtml">datasets::YOLOV2ConvolutionLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:af3310a6693b1d28b4d474e2a025b8777"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6faf0b684dd2c7e5bb111dd8f8f8c6f1"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6faf0b684dd2c7e5bb111dd8f8f8c6f1">REGISTER_FIXTURE_DATA_TEST_CASE</a> (Floor, <a class="el" href="namespacearm__compute_1_1test.xhtml#ac8cf6873b0e9ac7334bcbc042fdc5f02">NEFloorFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_small_shapes.xhtml">datasets::SmallShapes</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:a6faf0b684dd2c7e5bb111dd8f8f8c6f1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a16176f104b13866fa9c0379d3fd9ef1f"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a16176f104b13866fa9c0379d3fd9ef1f">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetFullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_fully_connected_layer_dataset.xhtml">datasets::AlexNetFullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a16176f104b13866fa9c0379d3fd9ef1f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad83dbea09b27679e5c1d7950ae035f3a"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad83dbea09b27679e5c1d7950ae035f3a">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_fully_connected_layer_dataset.xhtml">datasets::LeNet5FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ad83dbea09b27679e5c1d7950ae035f3a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a597d20f8105ae670eccdc44b0486ad4e"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a597d20f8105ae670eccdc44b0486ad4e">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_fully_connected_layer_dataset.xhtml">datasets::VGG16FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a597d20f8105ae670eccdc44b0486ad4e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a033a42b308036c4e46a8bef7536d88f9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a033a42b308036c4e46a8bef7536d88f9">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a033a42b308036c4e46a8bef7536d88f9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4b45a5a8afe0c81a4aafef1ba2ba96e8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4b45a5a8afe0c81a4aafef1ba2ba96e8">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a4b45a5a8afe0c81a4aafef1ba2ba96e8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4325316dca63988d0c63c8e761143557"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4325316dca63988d0c63c8e761143557">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetFullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_fully_connected_layer_dataset.xhtml">datasets::AlexNetFullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a4325316dca63988d0c63c8e761143557"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4ecb06077e2a789221648d0479e61809"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4ecb06077e2a789221648d0479e61809">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_fully_connected_layer_dataset.xhtml">datasets::LeNet5FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a4ecb06077e2a789221648d0479e61809"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:adeee41f0a436718ca296fc99f2e2a151"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#adeee41f0a436718ca296fc99f2e2a151">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_fully_connected_layer_dataset.xhtml">datasets::VGG16FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:adeee41f0a436718ca296fc99f2e2a151"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af5e14e7ca5ce517a75fb019b02108797"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af5e14e7ca5ce517a75fb019b02108797">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:af5e14e7ca5ce517a75fb019b02108797"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1d77d86fcdca1b8578756eae70fcac85"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1d77d86fcdca1b8578756eae70fcac85">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4FullyConnectedLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_fully_connected_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a1d77d86fcdca1b8578756eae70fcac85"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1d515029981b77ba7d02f20251013a3b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1d515029981b77ba7d02f20251013a3b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1GEMM, <a class="el" href="namespacearm__compute_1_1test.xhtml#a789c444c1307e85eec5f8b0d75fd5f7d">NEGEMMFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_g_e_m_m_dataset.xhtml">datasets::GoogLeNetInceptionV1GEMMDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:a1d515029981b77ba7d02f20251013a3b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5dbda869f12c5e1ffa17a2dce7e82609"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a5dbda869f12c5e1ffa17a2dce7e82609">REGISTER_FIXTURE_DATA_TEST_CASE</a> (MatrixMultiplyGEMM, <a class="el" href="namespacearm__compute_1_1test.xhtml#a789c444c1307e85eec5f8b0d75fd5f7d">NEGEMMFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_matrix_multiply_g_e_m_m_dataset.xhtml">datasets::MatrixMultiplyGEMMDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:a5dbda869f12c5e1ffa17a2dce7e82609"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac9eaa20c5215f43c16202896b7ea9118"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac9eaa20c5215f43c16202896b7ea9118">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogleNetGEMM, <a class="el" href="namespacearm__compute_1_1test.xhtml#a789c444c1307e85eec5f8b0d75fd5f7d">NEGEMMFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_google_net_g_e_m_m_dataset.xhtml">datasets::GoogleNetGEMMDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>))</td></tr>
+<tr class="separator:ac9eaa20c5215f43c16202896b7ea9118"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3d815590d056717dde89027c469fba5a"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a3d815590d056717dde89027c469fba5a">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#acc2c4764a300b505b50e9ba0642eff2b">NENormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_normalization_layer_dataset.xhtml">datasets::AlexNetNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a3d815590d056717dde89027c469fba5a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a04a6f03a4f0b85f507735cd409a8b74d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a04a6f03a4f0b85f507735cd409a8b74d">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1NormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#acc2c4764a300b505b50e9ba0642eff2b">NENormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1NormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a04a6f03a4f0b85f507735cd409a8b74d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a962c45074ad2b94899bc7003b3db0509"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a962c45074ad2b94899bc7003b3db0509">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetNormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#acc2c4764a300b505b50e9ba0642eff2b">NENormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_normalization_layer_dataset.xhtml">datasets::AlexNetNormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a962c45074ad2b94899bc7003b3db0509"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab17878545b689878d626f8e2298d2b1b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ab17878545b689878d626f8e2298d2b1b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1NormalizationLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#acc2c4764a300b505b50e9ba0642eff2b">NENormalizationLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_normalization_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1NormalizationLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ab17878545b689878d626f8e2298d2b1b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af33a8fe45c20501be3c2fa7aaa32bf26"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af33a8fe45c20501be3c2fa7aaa32bf26">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_pooling_layer_dataset.xhtml">datasets::AlexNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:af33a8fe45c20501be3c2fa7aaa32bf26"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7d7ea7b966b70d0931772f51a2cfcdb0"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a7d7ea7b966b70d0931772f51a2cfcdb0">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_pooling_layer_dataset.xhtml">datasets::LeNet5PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a7d7ea7b966b70d0931772f51a2cfcdb0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac4a14a4ebd0a6067fa657e06d6e6d9ec"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac4a14a4ebd0a6067fa657e06d6e6d9ec">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ac4a14a4ebd0a6067fa657e06d6e6d9ec"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a97400b4e200d00d86169b2afc584d2e3"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a97400b4e200d00d86169b2afc584d2e3">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a97400b4e200d00d86169b2afc584d2e3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa7edcfdce59bb3cb0f1ed784a28fb6d2"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa7edcfdce59bb3cb0f1ed784a28fb6d2">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_pooling_layer_dataset.xhtml">datasets::SqueezeNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:aa7edcfdce59bb3cb0f1ed784a28fb6d2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab644feafdb4a10f39c7e4acca32744eb"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ab644feafdb4a10f39c7e4acca32744eb">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_pooling_layer_dataset.xhtml">datasets::VGG16PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:ab644feafdb4a10f39c7e4acca32744eb"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a49f6de6126e559d77c77ec1252ead9e1"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a49f6de6126e559d77c77ec1252ead9e1">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_pooling_layer_dataset.xhtml">datasets::YOLOV2PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches", 1)))</td></tr>
+<tr class="separator:a49f6de6126e559d77c77ec1252ead9e1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8050efac909e6e8fce5791d2205fe0a8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a8050efac909e6e8fce5791d2205fe0a8">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_alex_net_pooling_layer_dataset.xhtml">datasets::AlexNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a8050efac909e6e8fce5791d2205fe0a8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0a1da94fb11977ec74784861c2c56246"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a0a1da94fb11977ec74784861c2c56246">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_pooling_layer_dataset.xhtml">datasets::LeNet5PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a0a1da94fb11977ec74784861c2c56246"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad5101e30d9b5306231c7ed2ce71f350b"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad5101e30d9b5306231c7ed2ce71f350b">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV1PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ad5101e30d9b5306231c7ed2ce71f350b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad0b2b0d1564cc6c5ac951a7b8e59bda8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad0b2b0d1564cc6c5ac951a7b8e59bda8">REGISTER_FIXTURE_DATA_TEST_CASE</a> (GoogLeNetInceptionV4PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_pooling_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:ad0b2b0d1564cc6c5ac951a7b8e59bda8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa973f66482fdadbd2ab72cdb6face4b5"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa973f66482fdadbd2ab72cdb6face4b5">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SqueezeNetPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_pooling_layer_dataset.xhtml">datasets::SqueezeNetPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:aa973f66482fdadbd2ab72cdb6face4b5"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9adba78f24e5c87b2c95a1c5e23883e9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9adba78f24e5c87b2c95a1c5e23883e9">REGISTER_FIXTURE_DATA_TEST_CASE</a> (VGG16PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_pooling_layer_dataset.xhtml">datasets::VGG16PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a9adba78f24e5c87b2c95a1c5e23883e9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a03474ce6764bea95de0edb583d281017"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a03474ce6764bea95de0edb583d281017">REGISTER_FIXTURE_DATA_TEST_CASE</a> (YOLOV2PoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_pooling_layer_dataset.xhtml">datasets::YOLOV2PoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a>), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{4, 8})))</td></tr>
+<tr class="separator:a03474ce6764bea95de0edb583d281017"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa14390b7bed93ce327f5dedd89fc8928"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa14390b7bed93ce327f5dedd89fc8928">REGISTER_FIXTURE_DATA_TEST_CASE</a> (SmallROIPoolingLayer, <a class="el" href="namespacearm__compute_1_1test.xhtml#a7ad74154ac625702bef70b90243ae63f">NEROIPoolingLayerFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="el" href="classarm__compute_1_1test_1_1datasets_1_1_small_r_o_i_pooling_layer_dataset.xhtml">datasets::SmallROIPoolingLayerDataset</a>(), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("DataType",{DataType::F32})), <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:aa14390b7bed93ce327f5dedd89fc8928"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4fa3f7aa92292c25a9876a3b1cded7c9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4fa3f7aa92292c25a9876a3b1cded7c9">REGISTER_FIXTURE_DATA_TEST_CASE</a> (AlexNet, <a class="el" href="namespacearm__compute_1_1test.xhtml#ae0e8bcf3b0ed15e708b4a38febfdb84e">NEAlexNetFixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(alex_net_data_types, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8})))</td></tr>
+<tr class="separator:a4fa3f7aa92292c25a9876a3b1cded7c9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9ba464da0fc25dbd0cb96fe5c61494c4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9ba464da0fc25dbd0cb96fe5c61494c4">REGISTER_FIXTURE_DATA_TEST_CASE</a> (LeNet5, <a class="el" href="namespacearm__compute_1_1test.xhtml#a6a292ad5fedcc7dea6c6eb1be6d4c0d3">NELeNet5Fixture</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfa5fb1f955b45e38e31789286a1790398d">framework::DatasetMode::ALL</a>, <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("Batches",{1, 4, 8}))</td></tr>
+<tr class="separator:a9ba464da0fc25dbd0cb96fe5c61494c4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a629633220b1b91a871c57b679b9f06e3"><td class="memTemplParams" colspan="2">template<typename O , typename F , typename... As> </td></tr>
+<tr class="memitem:a629633220b1b91a871c57b679b9f06e3"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a629633220b1b91a871c57b679b9f06e3">apply</a> (O *obj, F &&func, const std::tuple< As...> &args)</td></tr>
+<tr class="separator:a629633220b1b91a871c57b679b9f06e3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa18932675cbb5eb9c9dbf8ff4d7106c7"><td class="memTemplParams" colspan="2">template<typename T , typename std::enable_if< std::is_same< typename T::value_type, std::string >::value, int >::type = 0> </td></tr>
+<tr class="memitem:aa18932675cbb5eb9c9dbf8ff4d7106c7"><td class="memTemplItemLeft" align="right" valign="top">std::string </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa18932675cbb5eb9c9dbf8ff4d7106c7">join</a> (T first, T last, const std::string &separator)</td></tr>
+<tr class="memdesc:aa18932675cbb5eb9c9dbf8ff4d7106c7"><td class="mdescLeft"> </td><td class="mdescRight">Helper function to concatenate multiple strings. <a href="#aa18932675cbb5eb9c9dbf8ff4d7106c7">More...</a><br/></td></tr>
+<tr class="separator:aa18932675cbb5eb9c9dbf8ff4d7106c7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a898a0423aace06af0f3a18a26a972a1a"><td class="memTemplParams" colspan="2">template<typename T , typename UnaryOp > </td></tr>
+<tr class="memitem:a898a0423aace06af0f3a18a26a972a1a"><td class="memTemplItemLeft" align="right" valign="top">std::string </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a898a0423aace06af0f3a18a26a972a1a">join</a> (T &&first, T &&last, const std::string &separator, UnaryOp &&op)</td></tr>
+<tr class="memdesc:a898a0423aace06af0f3a18a26a972a1a"><td class="mdescLeft"> </td><td class="mdescRight">Helper function to concatenate multiple values. <a href="#a898a0423aace06af0f3a18a26a972a1a">More...</a><br/></td></tr>
+<tr class="separator:a898a0423aace06af0f3a18a26a972a1a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a69835710fc772315f4e65ce156034530"><td class="memTemplParams" colspan="2">template<typename T , typename std::enable_if< std::is_arithmetic< typename T::value_type >::value, int >::type = 0> </td></tr>
+<tr class="memitem:a69835710fc772315f4e65ce156034530"><td class="memTemplItemLeft" align="right" valign="top">std::string </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a69835710fc772315f4e65ce156034530">join</a> (T &&first, T &&last, const std::string &separator)</td></tr>
+<tr class="memdesc:a69835710fc772315f4e65ce156034530"><td class="mdescLeft"> </td><td class="mdescRight">Helper function to concatenate multiple values. <a href="#a69835710fc772315f4e65ce156034530">More...</a><br/></td></tr>
+<tr class="separator:a69835710fc772315f4e65ce156034530"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5b67cbf475b1e1d3bec9b0b937fdafac"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a5b67cbf475b1e1d3bec9b0b937fdafac">tolower</a> (std::string string)</td></tr>
+<tr class="memdesc:a5b67cbf475b1e1d3bec9b0b937fdafac"><td class="mdescLeft"> </td><td class="mdescRight">Convert string to lower case. <a href="#a5b67cbf475b1e1d3bec9b0b937fdafac">More...</a><br/></td></tr>
+<tr class="separator:a5b67cbf475b1e1d3bec9b0b937fdafac"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8939810976531494e8db1f491bf61a35"><td class="memTemplParams" colspan="2">template<typename D , typename T , typename... Ts> </td></tr>
+<tr class="memitem:a8939810976531494e8db1f491bf61a35"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a8939810976531494e8db1f491bf61a35">fill_tensors</a> (D &&dist, std::initializer_list< int > seeds, T &&tensor, Ts &&...other_tensors)</td></tr>
+<tr class="separator:a8939810976531494e8db1f491bf61a35"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a28edc8880596d14c099f3c2509efc8b3"><td class="memTemplParams" colspan="2">template<typename U > </td></tr>
+<tr class="memitem:a28edc8880596d14c099f3c2509efc8b3"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">swap</a> (<a class="el" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor</a>< U > &tensor1, <a class="el" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor</a>< U > &tensor2)</td></tr>
+<tr class="separator:a28edc8880596d14c099f3c2509efc8b3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af4bcf30f8c56f547f66d61c7c5ae01db"><td class="memTemplParams" colspan="2">template<typename T , typename = typename std::enable_if<std::is_floating_point<T>::value>::type> </td></tr>
+<tr class="memitem:af4bcf30f8c56f547f66d61c7c5ae01db"><td class="memTemplItemLeft" align="right" valign="top">T </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#af4bcf30f8c56f547f66d61c7c5ae01db">round_half_up</a> (T <a class="el" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>)</td></tr>
+<tr class="memdesc:af4bcf30f8c56f547f66d61c7c5ae01db"><td class="mdescLeft"> </td><td class="mdescRight">Round floating-point value with half value rounding to positive infinity. <a href="#af4bcf30f8c56f547f66d61c7c5ae01db">More...</a><br/></td></tr>
+<tr class="separator:af4bcf30f8c56f547f66d61c7c5ae01db"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad93bb148a873f19ad7692756e59617f4"><td class="memTemplParams" colspan="2">template<typename T , typename = typename std::enable_if<std::is_floating_point<T>::value>::type> </td></tr>
+<tr class="memitem:ad93bb148a873f19ad7692756e59617f4"><td class="memTemplItemLeft" align="right" valign="top">T </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad93bb148a873f19ad7692756e59617f4">round_half_even</a> (T <a class="el" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>, T epsilon=std::numeric_limits< T >::epsilon())</td></tr>
+<tr class="memdesc:ad93bb148a873f19ad7692756e59617f4"><td class="mdescLeft"> </td><td class="mdescRight">Round floating-point value with half value rounding to nearest even. <a href="#ad93bb148a873f19ad7692756e59617f4">More...</a><br/></td></tr>
+<tr class="separator:ad93bb148a873f19ad7692756e59617f4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa337ab76176f3c4193642ac6de3a61cf"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">get_format_for_channel</a> (<a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)</td></tr>
-<tr class="memdesc:aa337ab76176f3c4193642ac6de3a61cf"><td class="mdescLeft"> </td><td class="mdescRight">Look up the format corresponding to a channel. <a href="#aa337ab76176f3c4193642ac6de3a61cf">More...</a><br /></td></tr>
+<tr class="memdesc:aa337ab76176f3c4193642ac6de3a61cf"><td class="mdescLeft"> </td><td class="mdescRight">Look up the format corresponding to a channel. <a href="#aa337ab76176f3c4193642ac6de3a61cf">More...</a><br/></td></tr>
<tr class="separator:aa337ab76176f3c4193642ac6de3a61cf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac7dbe33793790fc37a5eda11ed6b0273"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac7dbe33793790fc37a5eda11ed6b0273">get_channel_format</a> (<a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)</td></tr>
-<tr class="memdesc:ac7dbe33793790fc37a5eda11ed6b0273"><td class="mdescLeft"> </td><td class="mdescRight">Return the format of a channel. <a href="#ac7dbe33793790fc37a5eda11ed6b0273">More...</a><br /></td></tr>
+<tr class="memdesc:ac7dbe33793790fc37a5eda11ed6b0273"><td class="mdescLeft"> </td><td class="mdescRight">Return the format of a channel. <a href="#ac7dbe33793790fc37a5eda11ed6b0273">More...</a><br/></td></tr>
<tr class="separator:ac7dbe33793790fc37a5eda11ed6b0273"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1ebbb23b0094d47c51226d58e17e6447"><td class="memTemplParams" colspan="2">template<typename F , typename T > </td></tr>
-<tr class="memitem:a1ebbb23b0094d47c51226d58e17e6447"><td class="memTemplItemLeft" align="right" valign="top">T </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1ebbb23b0094d47c51226d58e17e6447">foldl</a> (F &&, const T &value)</td></tr>
-<tr class="memdesc:a1ebbb23b0094d47c51226d58e17e6447"><td class="mdescLeft"> </td><td class="mdescRight">Base case of foldl. <a href="#a1ebbb23b0094d47c51226d58e17e6447">More...</a><br /></td></tr>
+<tr class="memitem:a1ebbb23b0094d47c51226d58e17e6447"><td class="memTemplItemLeft" align="right" valign="top">T </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1ebbb23b0094d47c51226d58e17e6447">foldl</a> (F &&, const T &<a class="el" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>)</td></tr>
+<tr class="memdesc:a1ebbb23b0094d47c51226d58e17e6447"><td class="mdescLeft"> </td><td class="mdescRight">Base case of foldl. <a href="#a1ebbb23b0094d47c51226d58e17e6447">More...</a><br/></td></tr>
<tr class="separator:a1ebbb23b0094d47c51226d58e17e6447"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad933f996ccb22854ae56dd86de8cbbfe"><td class="memTemplParams" colspan="2">template<typename F , typename T , typename U > </td></tr>
<tr class="memitem:ad933f996ccb22854ae56dd86de8cbbfe"><td class="memTemplItemLeft" align="right" valign="top">auto </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ad933f996ccb22854ae56dd86de8cbbfe">foldl</a> (F &&func, T &&value1, U &&value2) -> decltype(func(value1, value2))</td></tr>
-<tr class="memdesc:ad933f996ccb22854ae56dd86de8cbbfe"><td class="mdescLeft"> </td><td class="mdescRight">Base case of foldl. <a href="#ad933f996ccb22854ae56dd86de8cbbfe">More...</a><br /></td></tr>
+<tr class="memdesc:ad933f996ccb22854ae56dd86de8cbbfe"><td class="mdescLeft"> </td><td class="mdescRight">Base case of foldl. <a href="#ad933f996ccb22854ae56dd86de8cbbfe">More...</a><br/></td></tr>
<tr class="separator:ad933f996ccb22854ae56dd86de8cbbfe"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a89322cccde5e0a27d3a41085d3fd366c"><td class="memTemplParams" colspan="2">template<typename F , typename I , typename T , typename... Vs> </td></tr>
-<tr class="memitem:a89322cccde5e0a27d3a41085d3fd366c"><td class="memTemplItemLeft" align="right" valign="top">I </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a89322cccde5e0a27d3a41085d3fd366c">foldl</a> (F &&func, I &&initial, T &&value, Vs &&...values)</td></tr>
-<tr class="memdesc:a89322cccde5e0a27d3a41085d3fd366c"><td class="mdescLeft"> </td><td class="mdescRight">Fold left. <a href="#a89322cccde5e0a27d3a41085d3fd366c">More...</a><br /></td></tr>
+<tr class="memitem:a89322cccde5e0a27d3a41085d3fd366c"><td class="memTemplItemLeft" align="right" valign="top">I </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a89322cccde5e0a27d3a41085d3fd366c">foldl</a> (F &&func, I &&initial, T &&<a class="el" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>, Vs &&...values)</td></tr>
+<tr class="memdesc:a89322cccde5e0a27d3a41085d3fd366c"><td class="mdescLeft"> </td><td class="mdescRight">Fold left. <a href="#a89322cccde5e0a27d3a41085d3fd366c">More...</a><br/></td></tr>
<tr class="separator:a89322cccde5e0a27d3a41085d3fd366c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a670cba074b4be0bf9af03e48250bd616"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a670cba074b4be0bf9af03e48250bd616">shape_to_valid_region</a> (<a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape)</td></tr>
-<tr class="memdesc:a670cba074b4be0bf9af03e48250bd616"><td class="mdescLeft"> </td><td class="mdescRight">Create a valid region covering the enitre tensor shape. <a href="#a670cba074b4be0bf9af03e48250bd616">More...</a><br /></td></tr>
-<tr class="separator:a670cba074b4be0bf9af03e48250bd616"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6b97d7bba7b5cee833eb5c2282e8d246"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6b97d7bba7b5cee833eb5c2282e8d246">shape_to_valid_region_undefined_border</a> (<a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape, <a class="el" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a> border_size)</td></tr>
-<tr class="memdesc:a6b97d7bba7b5cee833eb5c2282e8d246"><td class="mdescLeft"> </td><td class="mdescRight">Create a valid region covering the tensor shape with UNDEFINED border mode and specified border size. <a href="#a6b97d7bba7b5cee833eb5c2282e8d246">More...</a><br /></td></tr>
-<tr class="separator:a6b97d7bba7b5cee833eb5c2282e8d246"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a356470553f2afd5673a41cf4da48e33b"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a356470553f2afd5673a41cf4da48e33b">required_padding</a> (int size, int step)</td></tr>
-<tr class="memdesc:a356470553f2afd5673a41cf4da48e33b"><td class="mdescLeft"> </td><td class="mdescRight">Calculate the required padding given the available <code>size</code> and the required. <a href="#a356470553f2afd5673a41cf4da48e33b">More...</a><br /></td></tr>
-<tr class="separator:a356470553f2afd5673a41cf4da48e33b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a08e86555c8b4d8ae148173d0bda4552f"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a08e86555c8b4d8ae148173d0bda4552f">required_padding_undefined_border_write</a> (int size, int step, int border_size)</td></tr>
-<tr class="memdesc:a08e86555c8b4d8ae148173d0bda4552f"><td class="mdescLeft"> </td><td class="mdescRight">Calculate the required padding for writing operation with UNDEFINED border mode. <a href="#a08e86555c8b4d8ae148173d0bda4552f">More...</a><br /></td></tr>
-<tr class="separator:a08e86555c8b4d8ae148173d0bda4552f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aaaa9677420848c94f3a8fd0c3bb0d1fc"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aaaa9677420848c94f3a8fd0c3bb0d1fc">required_padding_undefined_border_read</a> (int size, int read_step, int process_step)</td></tr>
-<tr class="memdesc:aaaa9677420848c94f3a8fd0c3bb0d1fc"><td class="mdescLeft"> </td><td class="mdescRight">Calculate the required padding for reading operation with UNDEFINED border mode. <a href="#aaaa9677420848c94f3a8fd0c3bb0d1fc">More...</a><br /></td></tr>
-<tr class="separator:aaaa9677420848c94f3a8fd0c3bb0d1fc"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4c9ad143c34306817986409ffb1dbd40"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4c9ad143c34306817986409ffb1dbd40">shape_to_valid_region</a> (<a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape, bool border_undefined=false, <a class="el" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a> border_size=<a class="el" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a>(0))</td></tr>
+<tr class="memdesc:a4c9ad143c34306817986409ffb1dbd40"><td class="mdescLeft"> </td><td class="mdescRight">Create a valid region based on tensor shape, border mode and border size. <a href="#a4c9ad143c34306817986409ffb1dbd40">More...</a><br/></td></tr>
+<tr class="separator:a4c9ad143c34306817986409ffb1dbd40"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1e6934e95738573214c2ce1d6648d116"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
-<tr class="memitem:a1e6934e95738573214c2ce1d6648d116"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a> (void *ptr, T value, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type)</td></tr>
-<tr class="memdesc:a1e6934e95738573214c2ce1d6648d116"><td class="mdescLeft"> </td><td class="mdescRight">Write the value after casting the pointer according to <code>data_type</code>. <a href="#a1e6934e95738573214c2ce1d6648d116">More...</a><br /></td></tr>
+<tr class="memitem:a1e6934e95738573214c2ce1d6648d116"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a> (void *ptr, T <a class="el" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type)</td></tr>
+<tr class="memdesc:a1e6934e95738573214c2ce1d6648d116"><td class="mdescLeft"> </td><td class="mdescRight">Write the value after casting the pointer according to <code>data_type</code>. <a href="#a1e6934e95738573214c2ce1d6648d116">More...</a><br/></td></tr>
<tr class="separator:a1e6934e95738573214c2ce1d6648d116"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4965b2f6821e0cf0afee738158bd8377"><td class="memTemplParams" colspan="2">template<typename U , typename T > </td></tr>
<tr class="memitem:a4965b2f6821e0cf0afee738158bd8377"><td class="memTemplItemLeft" align="right" valign="top">T </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4965b2f6821e0cf0afee738158bd8377">saturate_cast</a> (T val)</td></tr>
-<tr class="memdesc:a4965b2f6821e0cf0afee738158bd8377"><td class="mdescLeft"> </td><td class="mdescRight">Saturate a value of type T against the numeric limits of type U. <a href="#a4965b2f6821e0cf0afee738158bd8377">More...</a><br /></td></tr>
+<tr class="memdesc:a4965b2f6821e0cf0afee738158bd8377"><td class="mdescLeft"> </td><td class="mdescRight">Saturate a value of type T against the numeric limits of type U. <a href="#a4965b2f6821e0cf0afee738158bd8377">More...</a><br/></td></tr>
<tr class="separator:a4965b2f6821e0cf0afee738158bd8377"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a24d8c0391cfa38e78969b6ad97c0ff09"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a> (const <a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, int index)</td></tr>
-<tr class="memdesc:a24d8c0391cfa38e78969b6ad97c0ff09"><td class="mdescLeft"> </td><td class="mdescRight">Convert a linear index into n-dimensional coordinates. <a href="#a24d8c0391cfa38e78969b6ad97c0ff09">More...</a><br /></td></tr>
+<tr class="memdesc:a24d8c0391cfa38e78969b6ad97c0ff09"><td class="mdescLeft"> </td><td class="mdescRight">Convert a linear index into n-dimensional coordinates. <a href="#a24d8c0391cfa38e78969b6ad97c0ff09">More...</a><br/></td></tr>
<tr class="separator:a24d8c0391cfa38e78969b6ad97c0ff09"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9be4cb7e6ee20063a4a10bc3abb750b9"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a9be4cb7e6ee20063a4a10bc3abb750b9">coord2index</a> (const <a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, const <a class="el" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &coord)</td></tr>
-<tr class="memdesc:a9be4cb7e6ee20063a4a10bc3abb750b9"><td class="mdescLeft"> </td><td class="mdescRight">Linearise the given coordinate. <a href="#a9be4cb7e6ee20063a4a10bc3abb750b9">More...</a><br /></td></tr>
+<tr class="memdesc:a9be4cb7e6ee20063a4a10bc3abb750b9"><td class="mdescLeft"> </td><td class="mdescRight">Linearise the given coordinate. <a href="#a9be4cb7e6ee20063a4a10bc3abb750b9">More...</a><br/></td></tr>
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-<tr class="memitem:a58ee979a599b3b6a2587964106b1910c"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a58ee979a599b3b6a2587964106b1910c">is_in_valid_region</a> (const <a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> &valid_region, const <a class="el" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &coord)</td></tr>
-<tr class="memdesc:a58ee979a599b3b6a2587964106b1910c"><td class="mdescLeft"> </td><td class="mdescRight">Check if a coordinate is within a valid region. <a href="#a58ee979a599b3b6a2587964106b1910c">More...</a><br /></td></tr>
-<tr class="separator:a58ee979a599b3b6a2587964106b1910c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a856b55fc20ddcbdbeb84c35ae27bedac"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a856b55fc20ddcbdbeb84c35ae27bedac">is_in_valid_region</a> (const <a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> &valid_region, <a class="el" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> coord)</td></tr>
+<tr class="memdesc:a856b55fc20ddcbdbeb84c35ae27bedac"><td class="mdescLeft"> </td><td class="mdescRight">Check if a coordinate is within a valid region. <a href="#a856b55fc20ddcbdbeb84c35ae27bedac">More...</a><br/></td></tr>
+<tr class="separator:a856b55fc20ddcbdbeb84c35ae27bedac"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2ce249581879425cc66db8d364c838f3"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
+<tr class="memitem:a2ce249581879425cc66db8d364c838f3"><td class="memTemplItemLeft" align="right" valign="top">T </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a2ce249581879425cc66db8d364c838f3">create_tensor</a> (const <a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type, int num_channels=1, int fixed_point_position=0)</td></tr>
+<tr class="memdesc:a2ce249581879425cc66db8d364c838f3"><td class="mdescLeft"> </td><td class="mdescRight">Create and initialize a tensor of the given type. <a href="#a2ce249581879425cc66db8d364c838f3">More...</a><br/></td></tr>
+<tr class="separator:a2ce249581879425cc66db8d364c838f3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac7324cc960068b65c558b7d25dfe2914"><td class="memItemLeft" align="right" valign="top">std::vector< <a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac7324cc960068b65c558b7d25dfe2914">generate_random_rois</a> (const <a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, const <a class="el" href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml">ROIPoolingLayerInfo</a> &pool_info, unsigned int num_rois, std::random_device::result_type seed)</td></tr>
+<tr class="memdesc:ac7324cc960068b65c558b7d25dfe2914"><td class="mdescLeft"> </td><td class="mdescRight">Create a vector of random ROIs. <a href="#ac7324cc960068b65c558b7d25dfe2914">More...</a><br/></td></tr>
+<tr class="separator:ac7324cc960068b65c558b7d25dfe2914"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac35e7a1ad467f5fe8620cbbc5793d53b"><td class="memTemplParams" colspan="2">template<typename T , typename ArrayAccessor_T > </td></tr>
+<tr class="memitem:ac35e7a1ad467f5fe8620cbbc5793d53b"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ac35e7a1ad467f5fe8620cbbc5793d53b">fill_array</a> (ArrayAccessor_T &&array, const std::vector< T > &v)</td></tr>
+<tr class="separator:ac35e7a1ad467f5fe8620cbbc5793d53b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae47155d6186155ec4da9295764b3c05a"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae47155d6186155ec4da9295764b3c05a">get_typestring</a> (<a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type)</td></tr>
+<tr class="memdesc:ae47155d6186155ec4da9295764b3c05a"><td class="mdescLeft"> </td><td class="mdescRight">Obtain numpy type string from DataType. <a href="#ae47155d6186155ec4da9295764b3c05a">More...</a><br/></td></tr>
+<tr class="separator:ae47155d6186155ec4da9295764b3c05a"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="var-members"></a>
Variables</h2></td></tr>
-<tr class="memitem:a8f4fc4a0603d589eb1db7a8f539a6279"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structarm__compute_1_1test_1_1performance_1_1_performance_user_configuration.xhtml">PerformanceUserConfiguration</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a8f4fc4a0603d589eb1db7a8f539a6279">user_config</a></td></tr>
-<tr class="separator:a8f4fc4a0603d589eb1db7a8f539a6279"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a4ced6442a379a75e8a6c4be093fb666b"><td class="memItemLeft" align="right" valign="top">std::unique_ptr< <a class="el" href="classarm__compute_1_1test_1_1_tensor_library.xhtml">TensorLibrary</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a4ced6442a379a75e8a6c4be093fb666b">library</a></td></tr>
-<tr class="separator:a4ced6442a379a75e8a6c4be093fb666b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aab9a2ff74a27ae837d32a79a38952228"><td class="memItemLeft" align="right" valign="top">const auto </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a> = <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("DataType", { DataType::F32 })</td></tr>
+<tr class="separator:aab9a2ff74a27ae837d32a79a38952228"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a71326f0909d77386e29b511e1990a11f"><td class="memItemLeft" align="right" valign="top">std::unique_ptr< <a class="el" href="classarm__compute_1_1test_1_1_assets_library.xhtml">AssetsLibrary</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a></td></tr>
+<tr class="separator:a71326f0909d77386e29b511e1990a11f"><td class="memSeparator" colspan="2"> </td></tr>
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<h2 class="groupheader">Typedef Documentation</h2>
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- <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#aa1a629d971f45dc8c4cb7ec2d5c8728e">ActivationLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a><<a class="el" href="classarm__compute_1_1test_1_1_activation_layer_data_object.xhtml">ActivationLayerDataObject</a>, Size></td>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a74a10374253178ae54e1baab173698a1">CLActivationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">ActivationLayerFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">CLActivationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
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-<p>Definition at line <a class="el" href="_activation_layer_dataset_8h_source.xhtml#l00062">62</a> of file <a class="el" href="_activation_layer_dataset_8h_source.xhtml">ActivationLayerDataset.h</a>.</p>
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_activation_layer_8cpp_source.xhtml#l00051">51</a> of file <a class="el" href="benchmark_2_c_l_2_activation_layer_8cpp_source.xhtml">ActivationLayer.cpp</a>.</p>
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- <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a6b006671b13de04edb1001f32966d9de">BatchNormalizationLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a><<a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_data_object.xhtml">BatchNormalizationLayerDataObject</a>, Size></td>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#aa631c5ec3d7cb3dab649f994e9e9217d">CLAlexNetFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_alex_net_fixture.xhtml">AlexNetFixture</a><<a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_sub_tensor.xhtml">CLSubTensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>, <a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">CLActivationLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_convolution_layer.xhtml">CLConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_direct_convolution_layer.xhtml">CLDirectConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_normalization_layer.xhtml">CLNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_pooling_layer.xhtml">CLPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_softmax_layer.xhtml">CLSoftmaxLayer</a>></td>
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-<p>Definition at line <a class="el" href="_batch_normalization_layer_dataset_8h_source.xhtml#l00070">70</a> of file <a class="el" href="_batch_normalization_layer_dataset_8h_source.xhtml">BatchNormalizationLayerDataset.h</a>.</p>
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_s_y_s_t_e_m_2_alex_net_8cpp_source.xhtml#l00056">56</a> of file <a class="el" href="benchmark_2_c_l_2_s_y_s_t_e_m_2_alex_net_8cpp_source.xhtml">AlexNet.cpp</a>.</p>
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- <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a18ed03ad3383da62aec512f04372d2f7">ConvolutionLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a><<a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_data_object.xhtml">ConvolutionLayerDataObject</a>, Size></td>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#af80ea91532f0ebdccb3f1d8e507a98ad">CLBatchNormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_fixture.xhtml">BatchNormalizationLayerFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_batch_normalization_layer.xhtml">CLBatchNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
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-<p>Definition at line <a class="el" href="_convolution_layer_dataset_8h_source.xhtml#l00073">73</a> of file <a class="el" href="_convolution_layer_dataset_8h_source.xhtml">ConvolutionLayerDataset.h</a>.</p>
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00046">46</a> of file <a class="el" href="benchmark_2_c_l_2_batch_normalization_layer_8cpp_source.xhtml">BatchNormalizationLayer.cpp</a>.</p>
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- <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#ad41fe013a10729c8790f3d30bfeac8f1">FullyConnectedLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a><<a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_data_object.xhtml">FullyConnectedLayerDataObject</a>, Size></td>
+ <td class="memname">typedef <a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">ConvolutionLayerFixture</a>< <a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_direct_convolution_layer.xhtml">CLDirectConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a> > <a class="el" href="namespacearm__compute_1_1test.xhtml#ad275d75e1b63f91fdc59afe026688b12">CLConvolutionLayerFixture</a></td>
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-<p>Definition at line <a class="el" href="_fully_connected_layer_dataset_8h_source.xhtml#l00071">71</a> of file <a class="el" href="_fully_connected_layer_dataset_8h_source.xhtml">FullyConnectedLayerDataset.h</a>.</p>
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00051">51</a> of file <a class="el" href="benchmark_2_c_l_2_convolution_layer_8cpp_source.xhtml">ConvolutionLayer.cpp</a>.</p>
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- <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a62dd75dba5587b539e722ea857347c26">NormalizationLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a><<a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_data_object.xhtml">NormalizationLayerDataObject</a>, Size></td>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a1e3870d2e47dfd84b259bdbff0a6f5f8">CLDepthwiseConvolutionFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_depthwise_convolution_fixture.xhtml">DepthwiseConvolutionFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_depthwise_convolution.xhtml">CLDepthwiseConvolution</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
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-<p>Definition at line <a class="el" href="_normalization_layer_dataset_8h_source.xhtml#l00063">63</a> of file <a class="el" href="_normalization_layer_dataset_8h_source.xhtml">NormalizationLayerDataset.h</a>.</p>
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_depthwise_convolution_8cpp_source.xhtml#l00041">41</a> of file <a class="el" href="benchmark_2_c_l_2_depthwise_convolution_8cpp_source.xhtml">DepthwiseConvolution.cpp</a>.</p>
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- <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#af11de09f727dca1d78602572247fa5ff">PoolingLayerDataset</a> = <a class="el" href="classarm__compute_1_1test_1_1_generic_dataset.xhtml">GenericDataset</a><<a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_data_object.xhtml">PoolingLayerDataObject</a>, Size></td>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#adc07e82b4049d653c965af2606a7d70f">CLDepthwiseSeparableConvolutionLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_depthwise_separable_convolution_layer_fixture.xhtml">DepthwiseSeparableConvolutionLayerFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_depthwise_separable_convolution_layer.xhtml">CLDepthwiseSeparableConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
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-<p>Definition at line <a class="el" href="_pooling_layer_dataset_8h_source.xhtml#l00070">70</a> of file <a class="el" href="_pooling_layer_dataset_8h_source.xhtml">PoolingLayerDataset.h</a>.</p>
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_depthwise_separable_convolution_layer_8cpp_source.xhtml#l00041">41</a> of file <a class="el" href="benchmark_2_c_l_2_depthwise_separable_convolution_layer_8cpp_source.xhtml">DepthwiseSeparableConvolutionLayer.cpp</a>.</p>
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+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a4a14e383a632057e99845c74a72a6454">CLFloorFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_floor_fixture.xhtml">FloorFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_floor.xhtml">CLFloor</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
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+ </table>
+</div><div class="memdoc">
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+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_floor_8cpp_source.xhtml#l00045">45</a> of file <a class="el" href="benchmark_2_c_l_2_floor_8cpp_source.xhtml">Floor.cpp</a>.</p>
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+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a4c33955ce3f6ed3a4d756cdebf6c8b3a">CLFullyConnectedLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_fixture.xhtml">FullyConnectedLayerFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_fully_connected_layer_8cpp_source.xhtml#l00049">49</a> of file <a class="el" href="benchmark_2_c_l_2_fully_connected_layer_8cpp_source.xhtml">FullyConnectedLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="abf07c2bf7d8e9c76e146f9b21bee88fd"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#abf07c2bf7d8e9c76e146f9b21bee88fd">CLGEMMFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_g_e_m_m_fixture.xhtml">GEMMFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml">CLGEMM</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00046">46</a> of file <a class="el" href="benchmark_2_c_l_2_g_e_m_m_8cpp_source.xhtml">GEMM.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="ae3b678c8477dd5acc5e264eae37b562c"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#ae3b678c8477dd5acc5e264eae37b562c">CLLeNet5Fixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_le_net5_fixture.xhtml">LeNet5Fixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>, <a class="el" href="classarm__compute_1_1_c_l_activation_layer.xhtml">CLActivationLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_convolution_layer.xhtml">CLConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_pooling_layer.xhtml">CLPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_c_l_softmax_layer.xhtml">CLSoftmaxLayer</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_s_y_s_t_e_m_2_le_net5_8cpp_source.xhtml#l00049">49</a> of file <a class="el" href="benchmark_2_c_l_2_s_y_s_t_e_m_2_le_net5_8cpp_source.xhtml">LeNet5.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="af4f1c6ad288931f07f614316f57ed63b"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#af4f1c6ad288931f07f614316f57ed63b">CLNormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_fixture.xhtml">NormalizationLayerFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_normalization_layer.xhtml">CLNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_normalization_layer_8cpp_source.xhtml#l00046">46</a> of file <a class="el" href="benchmark_2_c_l_2_normalization_layer_8cpp_source.xhtml">NormalizationLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="a9c81648f3199d0d1c3f34a29a7a2bb8d"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a9c81648f3199d0d1c3f34a29a7a2bb8d">CLPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_fixture.xhtml">PoolingLayerFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_pooling_layer.xhtml">CLPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_pooling_layer_8cpp_source.xhtml#l00051">51</a> of file <a class="el" href="benchmark_2_c_l_2_pooling_layer_8cpp_source.xhtml">PoolingLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="a41884dec2ecae6674396802641b01060"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a41884dec2ecae6674396802641b01060">CLROIPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_r_o_i_pooling_layer_fixture.xhtml">ROIPoolingLayerFixture</a><<a class="el" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a>, <a class="el" href="classarm__compute_1_1_c_l_r_o_i_pooling_layer.xhtml">CLROIPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_accessor.xhtml">CLAccessor</a>, <a class="el" href="classarm__compute_1_1_c_l_array.xhtml">CLArray</a><<a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a>>, <a class="el" href="classarm__compute_1_1test_1_1_c_l_array_accessor.xhtml">CLArrayAccessor</a><<a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a>>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_c_l_2_r_o_i_pooling_layer_8cpp_source.xhtml#l00042">42</a> of file <a class="el" href="_c_l_2_r_o_i_pooling_layer_8cpp_source.xhtml">ROIPoolingLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="aeded391cb7ec7a44c41eb23544265894"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894">NEActivationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">ActivationLayerFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_activation_layer_8cpp_source.xhtml#l00055">55</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_activation_layer_8cpp_source.xhtml">ActivationLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="ae0e8bcf3b0ed15e708b4a38febfdb84e"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#ae0e8bcf3b0ed15e708b4a38febfdb84e">NEAlexNetFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_alex_net_fixture.xhtml">AlexNetFixture</a><<a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a>, <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_sub_tensor.xhtml">SubTensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>, <a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">NEConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">NEConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_normalization_layer.xhtml">NENormalizationLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_pooling_layer.xhtml">NEPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml">NESoftmaxLayer</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_s_y_s_t_e_m_2_alex_net_8cpp_source.xhtml#l00065">65</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_s_y_s_t_e_m_2_alex_net_8cpp_source.xhtml">AlexNet.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="ac7369c169e6de526fcb6f68e4a959444"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#ac7369c169e6de526fcb6f68e4a959444">NEBatchNormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_batch_normalization_layer_fixture.xhtml">BatchNormalizationLayerFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_batch_normalization_layer.xhtml">NEBatchNormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_batch_normalization_layer_8cpp_source.xhtml#l00051">51</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_batch_normalization_layer_8cpp_source.xhtml">BatchNormalizationLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="a3168ad22b6ac1e9a6996b53e5038a7a2"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">typedef <a class="el" href="classarm__compute_1_1test_1_1_convolution_layer_fixture.xhtml">ConvolutionLayerFixture</a>< <a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_direct_convolution_layer.xhtml">NEDirectConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a> > <a class="el" href="namespacearm__compute_1_1test.xhtml#a3168ad22b6ac1e9a6996b53e5038a7a2">NEConvolutionLayerFixture</a></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_convolution_layer_8cpp_source.xhtml#l00055">55</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_convolution_layer_8cpp_source.xhtml">ConvolutionLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="ac8cf6873b0e9ac7334bcbc042fdc5f02"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#ac8cf6873b0e9ac7334bcbc042fdc5f02">NEFloorFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_floor_fixture.xhtml">FloorFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_floor.xhtml">NEFloor</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_floor_8cpp_source.xhtml#l00045">45</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_floor_8cpp_source.xhtml">Floor.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="a0b4f7a523ddb2b823750ff5bdc03470c"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a0b4f7a523ddb2b823750ff5bdc03470c">NEFullyConnectedLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_fully_connected_layer_fixture.xhtml">FullyConnectedLayerFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_fully_connected_layer_8cpp_source.xhtml#l00053">53</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_fully_connected_layer_8cpp_source.xhtml">FullyConnectedLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="a789c444c1307e85eec5f8b0d75fd5f7d"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a789c444c1307e85eec5f8b0d75fd5f7d">NEGEMMFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_g_e_m_m_fixture.xhtml">GEMMFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_g_e_m_m_8cpp_source.xhtml#l00054">54</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_g_e_m_m_8cpp_source.xhtml">GEMM.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="a6a292ad5fedcc7dea6c6eb1be6d4c0d3"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a6a292ad5fedcc7dea6c6eb1be6d4c0d3">NELeNet5Fixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_le_net5_fixture.xhtml">LeNet5Fixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>, <a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_convolution_layer.xhtml">NEConvolutionLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_pooling_layer.xhtml">NEPoolingLayer</a>, <a class="el" href="classarm__compute_1_1_n_e_softmax_layer.xhtml">NESoftmaxLayer</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_s_y_s_t_e_m_2_le_net5_8cpp_source.xhtml#l00049">49</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_s_y_s_t_e_m_2_le_net5_8cpp_source.xhtml">LeNet5.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="acc2c4764a300b505b50e9ba0642eff2b"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#acc2c4764a300b505b50e9ba0642eff2b">NENormalizationLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_normalization_layer_fixture.xhtml">NormalizationLayerFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_normalization_layer.xhtml">NENormalizationLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_normalization_layer_8cpp_source.xhtml#l00049">49</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_normalization_layer_8cpp_source.xhtml">NormalizationLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="aafcc5ee5a13d9ed18d31591bb1d50fb0"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#aafcc5ee5a13d9ed18d31591bb1d50fb0">NEPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_pooling_layer_fixture.xhtml">PoolingLayerFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_pooling_layer.xhtml">NEPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="benchmark_2_n_e_o_n_2_pooling_layer_8cpp_source.xhtml#l00055">55</a> of file <a class="el" href="benchmark_2_n_e_o_n_2_pooling_layer_8cpp_source.xhtml">PoolingLayer.cpp</a>.</p>
+
+</div>
+</div>
+<a class="anchor" id="a7ad74154ac625702bef70b90243ae63f"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="namespacearm__compute_1_1test.xhtml#a7ad74154ac625702bef70b90243ae63f">NEROIPoolingLayerFixture</a> = <a class="el" href="classarm__compute_1_1test_1_1_r_o_i_pooling_layer_fixture.xhtml">ROIPoolingLayerFixture</a><<a class="el" href="classarm__compute_1_1_tensor.xhtml">Tensor</a>, <a class="el" href="classarm__compute_1_1_n_e_r_o_i_pooling_layer.xhtml">NEROIPoolingLayer</a>, <a class="el" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>, <a class="el" href="classarm__compute_1_1_array.xhtml">Array</a><<a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a>>, <a class="el" href="classarm__compute_1_1test_1_1_array_accessor.xhtml">ArrayAccessor</a><<a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a>>></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_n_e_o_n_2_r_o_i_pooling_layer_8cpp_source.xhtml#l00042">42</a> of file <a class="el" href="_n_e_o_n_2_r_o_i_pooling_layer_8cpp_source.xhtml">ROIPoolingLayer.cpp</a>.</p>
</div>
</div>
<h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a629633220b1b91a871c57b679b9f06e3"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void arm_compute::test::apply </td>
+ <td>(</td>
+ <td class="paramtype">O * </td>
+ <td class="paramname"><em>obj</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">F && </td>
+ <td class="paramname"><em>func</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const std::tuple< As...> & </td>
+ <td class="paramname"><em>args</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00079">79</a> of file <a class="el" href="tests_2framework_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+
+<p>References <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00072">arm_compute::test::framework::apply_impl()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> {</div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a8daf3ad5a8666ce417ad176256a592eb">detail::apply_impl</a>(obj, std::forward<F>(func), <a class="code" href="namespacecaffe__data__extractor.xhtml#aad3cdfd6574de97bf37448087aaff11d">args</a>, detail::sequence_t<<span class="keyword">sizeof</span>...(As)>());</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_xhtml_a8daf3ad5a8666ce417ad176256a592eb"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework.xhtml#a8daf3ad5a8666ce417ad176256a592eb">arm_compute::test::framework::apply_impl</a></div><div class="ttdeci">void apply_impl(O *obj, F &&func, const std::tuple< As...> &args, detail::sequence< S...>)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2framework_2_utils_8h_source.xhtml#l00072">Utils.h:72</a></div></div>
+<div class="ttc" id="namespacecaffe__data__extractor_xhtml_aad3cdfd6574de97bf37448087aaff11d"><div class="ttname"><a href="namespacecaffe__data__extractor.xhtml#aad3cdfd6574de97bf37448087aaff11d">caffe_data_extractor.args</a></div><div class="ttdeci">tuple args</div><div class="ttdef"><b>Definition:</b> <a href="caffe__data__extractor_8py_source.xhtml#l00021">caffe_data_extractor.py:21</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
<a class="anchor" id="a9be4cb7e6ee20063a4a10bc3abb750b9"></a>
<div class="memitem">
<div class="memproto">
@@ -494,13 +1036,13 @@
<tr>
<td class="memname">int arm_compute::test::coord2index </td>
<td>(</td>
- <td class="paramtype">const <a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> & </td>
+ <td class="paramtype">const TensorShape & </td>
<td class="paramname"><em>shape</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const <a class="el" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & </td>
+ <td class="paramtype">const Coordinates & </td>
<td class="paramname"><em>coord</em> </td>
</tr>
<tr>
@@ -527,12 +1069,192 @@
</dl>
<dl class="section return"><dt>Returns</dt><dd>Linear offset to the element. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00640">640</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00337">337</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="_error_8h_source.xhtml#l00115">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, and <a class="el" href="_tensor_shape_8h_source.xhtml#l00106">TensorShape::total_size()</a>.</p>
+<p>References <a class="el" href="_error_8h_source.xhtml#l00115">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, and <a class="el" href="_tensor_shape_8h_source.xhtml#l00135">TensorShape::total_size()</a>.</p>
-<p>Referenced by <a class="el" href="_raw_tensor_8cpp_source.xhtml#l00158">RawTensor::operator()()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> {</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(shape.total_size() == 0, <span class="stringliteral">"Cannot get index from empty shape"</span>);</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(coord.num_dimensions() == 0, <span class="stringliteral">"Cannot get index of empty coordinate"</span>);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keywordtype">int</span> index = 0;</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordtype">int</span> dim_size = 1;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span> </div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < coord.num_dimensions(); ++i)</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  {</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  index += coord[i] * dim_size;</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  dim_size *= shape[i];</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  }</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> </div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordflow">return</span> index;</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> }</div><div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00115">Error.h:115</a></div></div>
+<p>Referenced by <a class="el" href="tests_2validation_2_c_p_p_2_utils_8h_source.xhtml#l00081">arm_compute::test::validation::apply_2d_spatial_filter()</a>, <a class="el" href="_raw_tensor_8cpp_source.xhtml#l00057">RawTensor::operator()()</a>, <a class="el" href="_simple_tensor_8h_source.xhtml#l00323">SimpleTensor< T >::operator()()</a>, <a class="el" href="_c_p_p_2_scale_8cpp_source.xhtml#l00039">arm_compute::test::validation::reference::scale()</a>, and <a class="el" href="tests_2validation_2_c_p_p_2_utils_8h_source.xhtml#l00046">arm_compute::test::validation::tensor_elem_at()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> {</div>
+<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.total_size() == 0, <span class="stringliteral">"Cannot get index from empty shape"</span>);</div>
+<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(coord.num_dimensions() == 0, <span class="stringliteral">"Cannot get index of empty coordinate"</span>);</div>
+<div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div>
+<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordtype">int</span> index = 0;</div>
+<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordtype">int</span> dim_size = 1;</div>
+<div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div>
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < coord.num_dimensions(); ++i)</div>
+<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div>
+<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  index += coord[i] * dim_size;</div>
+<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  dim_size *= <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[i];</div>
+<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  }</div>
+<div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div>
+<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordflow">return</span> index;</div>
+<div class="line"><a name="l00352"></a><span class="lineno"> 352</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00089">MinMaxLocation.cpp:89</a></div></div>
+<div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00115">Error.h:115</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a2ce249581879425cc66db8d364c838f3"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">T arm_compute::test::create_tensor </td>
+ <td>(</td>
+ <td class="paramtype">const TensorShape & </td>
+ <td class="paramname"><em>shape</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">DataType </td>
+ <td class="paramname"><em>data_type</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>num_channels</em> = <code>1</code>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>fixed_point_position</em> = <code>0</code> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Create and initialize a tensor of the given type. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramdir">[in]</td><td class="paramname">shape</td><td><a class="el" href="classarm__compute_1_1_tensor.xhtml" title="Basic implementation of the tensor interface. ">Tensor</a> shape. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">data_type</td><td>Data type. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">num_channels</td><td>(Optional) Number of channels. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">fixed_point_position</td><td>(Optional) Number of fractional bits.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Initialized tensor of given type. </dd></dl>
+
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00378">378</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<div class="fragment"><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> {</div>
+<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  T tensor;</div>
+<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  tensor.allocator()->init(TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, num_channels, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">data_type</a>, fixed_point_position));</div>
+<div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div>
+<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">return</span> tensor;</div>
+<div class="line"><a name="l00384"></a><span class="lineno"> 384</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00089">MinMaxLocation.cpp:89</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2ad7f431e3446fddcd9b6b9f93c4c14"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00090">MinMaxLocation.cpp:90</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="ac35e7a1ad467f5fe8620cbbc5793d53b"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">void arm_compute::test::fill_array </td>
+ <td>(</td>
+ <td class="paramtype">ArrayAccessor_T && </td>
+ <td class="paramname"><em>array</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const std::vector< T > & </td>
+ <td class="paramname"><em>v</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00433">433</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+
+<p>Referenced by <a class="el" href="_r_o_i_pooling_layer_fixture_8h_source.xhtml#l00045">ROIPoolingLayerFixture< TensorType, Function, Accessor, Array_T, ArrayAccessor >::setup()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> {</div>
+<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  array.resize(v.size());</div>
+<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  std::memcpy(array.buffer(), v.data(), v.size() * <span class="keyword">sizeof</span>(T));</div>
+<div class="line"><a name="l00437"></a><span class="lineno"> 437</span> }</div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a8939810976531494e8db1f491bf61a35"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void arm_compute::test::fill_tensors </td>
+ <td>(</td>
+ <td class="paramtype">D && </td>
+ <td class="paramname"><em>dist</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">std::initializer_list< int > </td>
+ <td class="paramname"><em>seeds</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">T && </td>
+ <td class="paramname"><em>tensor</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">Ts &&... </td>
+ <td class="paramname"><em>other_tensors</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Definition at line <a class="el" href="_helper_8h_source.xhtml#l00039">39</a> of file <a class="el" href="_helper_8h_source.xhtml">Helper.h</a>.</p>
+
+<p>References <a class="el" href="_error_8h_source.xhtml#l00124">ARM_COMPUTE_ERROR_ON</a>, and <a class="el" href="main_8cpp_source.xhtml#l00054">library</a>.</p>
+<div class="fragment"><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> {</div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> std::array < T, 1 + <span class="keyword">sizeof</span>...(Ts) > tensors{ { std::forward<T>(tensor), std::forward<Ts>(other_tensors)... } };</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  std::vector<int> vs(seeds);</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(vs.size() != tensors.size());</div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">int</span> k = 0;</div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> tp : tensors)</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>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(*tp), std::forward<D>(dist), vs[k++]);</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> }</div>
+<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00124">Error.h:124</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a71326f0909d77386e29b511e1990a11f"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">arm_compute::test::library</a></div><div class="ttdeci">std::unique_ptr< AssetsLibrary > library</div><div class="ttdef"><b>Definition:</b> <a href="main_8cpp_source.xhtml#l00054">main.cpp:54</a></div></div>
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@@ -571,10 +1293,16 @@
<p>Base case of foldl. </p>
<dl class="section return"><dt>Returns</dt><dd>value. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00418">418</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00156">156</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>Referenced by <a class="el" href="tests_2_utils_8h_source.xhtml#l00441">foldl()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> {</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">return</span> value;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> }</div></div><!-- fragment -->
+<p>References <a class="el" href="hwc_8hpp_source.xhtml#l00269">value</a>.</p>
+
+<p>Referenced by <a class="el" href="tests_2_utils_8h_source.xhtml#l00179">foldl()</a>.</p>
+<div class="fragment"><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>  <span class="keywordflow">return</span> <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> }</div>
+<div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00269">hwc.hpp:269</a></div></div>
+</div><!-- fragment -->
</div>
</div>
<a class="anchor" id="ad933f996ccb22854ae56dd86de8cbbfe"></a>
@@ -619,8 +1347,11 @@
<p>Base case of foldl. </p>
<dl class="section return"><dt>Returns</dt><dd>func(value1, value2). </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00428">428</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<div class="fragment"><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">return</span> func(value1, value2);</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> }</div></div><!-- fragment -->
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00166">166</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<div class="fragment"><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> {</div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">return</span> func(value1, value2);</div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span> }</div>
+</div><!-- fragment -->
</div>
</div>
<a class="anchor" id="a89322cccde5e0a27d3a41085d3fd366c"></a>
@@ -678,10 +1409,116 @@
</dd>
</dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00441">441</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00179">179</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="tests_2_utils_8h_source.xhtml#l00418">foldl()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> {</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a89322cccde5e0a27d3a41085d3fd366c">foldl</a>(std::forward<F>(func), func(std::forward<I>(initial), std::forward<T>(value)), std::forward<Vs>(values)...);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_xhtml_a89322cccde5e0a27d3a41085d3fd366c"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a89322cccde5e0a27d3a41085d3fd366c">arm_compute::test::foldl</a></div><div class="ttdeci">I foldl(F &&func, I &&initial, T &&value, Vs &&...values)</div><div class="ttdoc">Fold left. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00441">Utils.h:441</a></div></div>
+<p>References <a class="el" href="tests_2_utils_8h_source.xhtml#l00156">foldl()</a>.</p>
+<div class="fragment"><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>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a89322cccde5e0a27d3a41085d3fd366c">foldl</a>(std::forward<F>(func), func(std::forward<I>(initial), std::forward<T>(<a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>)), std::forward<Vs>(values)...);</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a89322cccde5e0a27d3a41085d3fd366c"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a89322cccde5e0a27d3a41085d3fd366c">arm_compute::test::foldl</a></div><div class="ttdeci">I foldl(F &&func, I &&initial, T &&value, Vs &&...values)</div><div class="ttdoc">Fold left. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00179">Utils.h:179</a></div></div>
+<div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00269">hwc.hpp:269</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="ac7324cc960068b65c558b7d25dfe2914"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">std::vector<<a class="el" href="structarm__compute_1_1_r_o_i.xhtml">ROI</a>> arm_compute::test::generate_random_rois </td>
+ <td>(</td>
+ <td class="paramtype">const TensorShape & </td>
+ <td class="paramname"><em>shape</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const ROIPoolingLayerInfo & </td>
+ <td class="paramname"><em>pool_info</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">unsigned int </td>
+ <td class="paramname"><em>num_rois</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">std::random_device::result_type </td>
+ <td class="paramname"><em>seed</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Create a vector of random ROIs. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramdir">[in]</td><td class="paramname">shape</td><td>The shape of the input tensor. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">pool_info</td><td>The <a class="el" href="structarm__compute_1_1_r_o_i.xhtml" title="Region of interest. ">ROI</a> pooling information. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">num_rois</td><td>The number of ROIs to be created. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">seed</td><td>The random seed to be used.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A vector that contains the requested number of random ROIs </dd></dl>
+
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00395">395</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+
+<p>References <a class="el" href="_error_8h_source.xhtml#l00124">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00312">ROI::batch_idx</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00290">Rectangle::height</a>, <a class="el" href="tests_2validation_2_fixed_point_8h_source.xhtml#l00889">arm_compute::test::fixed_point_arithmetic::detail::max()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00495">ROIPoolingLayerInfo::pooled_height()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00491">ROIPoolingLayerInfo::pooled_width()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00311">ROI::rect</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00499">ROIPoolingLayerInfo::spatial_scale()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00289">Rectangle::width</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions< T >::x()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00287">Rectangle::x</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00086">Dimensions< T >::y()</a>, and <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00288">Rectangle::y</a>.</p>
+
+<p>Referenced by <a class="el" href="_r_o_i_pooling_layer_fixture_8h_source.xhtml#l00045">ROIPoolingLayerFixture< TensorType, Function, Accessor, Array_T, ArrayAccessor >::setup()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> {</div>
+<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((pool_info.pooled_width() < 4) || (pool_info.pooled_height() < 4));</div>
+<div class="line"><a name="l00398"></a><span class="lineno"> 398</span> </div>
+<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  std::vector<ROI> rois;</div>
+<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  std::mt19937 gen(seed);</div>
+<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pool_width = pool_info.pooled_width();</div>
+<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pool_height = pool_info.pooled_height();</div>
+<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> roi_scale = pool_info.spatial_scale();</div>
+<div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div>
+<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="comment">// Calculate distribution bounds</span></div>
+<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <span class="keyword">auto</span> scaled_width = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.x() / roi_scale) / pool_width);</div>
+<div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> <span class="keyword">auto</span> scaled_height = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.y() / roi_scale) / pool_height);</div>
+<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keyword">const</span> <span class="keyword">auto</span> min_width = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(pool_width / roi_scale);</div>
+<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keyword">const</span> <span class="keyword">auto</span> min_height = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(pool_height / roi_scale);</div>
+<div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div>
+<div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="comment">// Create distributions</span></div>
+<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  std::uniform_int_distribution<int> dist_batch(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[3] - 1);</div>
+<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  std::uniform_int_distribution<int> dist_x(0, scaled_width);</div>
+<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  std::uniform_int_distribution<int> dist_y(0, scaled_height);</div>
+<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  std::uniform_int_distribution<int> dist_w(min_width, <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::max</a>(min_width, (pool_width - 2) * scaled_width));</div>
+<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  std::uniform_int_distribution<int> dist_h(min_height, <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::max</a>(min_height, (pool_height - 2) * scaled_height));</div>
+<div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div>
+<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r = 0; r < num_rois; ++r)</div>
+<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  {</div>
+<div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  ROI roi;</div>
+<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  roi.batch_idx = dist_batch(gen);</div>
+<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  roi.rect.x = dist_x(gen);</div>
+<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  roi.rect.y = dist_y(gen);</div>
+<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  roi.rect.width = dist_w(gen);</div>
+<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  roi.rect.height = dist_h(gen);</div>
+<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  rois.push_back(roi);</div>
+<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  }</div>
+<div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div>
+<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordflow">return</span> rois;</div>
+<div class="line"><a name="l00430"></a><span class="lineno"> 430</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00089">MinMaxLocation.cpp:89</a></div></div>
+<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00124">Error.h:124</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00889">FixedPoint.h:889</a></div></div>
</div><!-- fragment -->
</div>
</div>
@@ -695,7 +1532,7 @@
<tr>
<td class="memname"><a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> arm_compute::test::get_channel_format </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> </td>
+ <td class="paramtype">Channel </td>
<td class="paramname"><em>channel</em></td><td>)</td>
<td></td>
</tr>
@@ -716,12 +1553,21 @@
</dl>
<dl class="section return"><dt>Returns</dt><dd>Format of the given channel. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00400">400</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00138">138</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
<p>References <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa9d5ed678fe57bcca610140957afab571">arm_compute::B</a>, <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aadfcf28d0734569a6a693bc8194de62bf">arm_compute::G</a>, <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aae1e1d3d40573127e9ee0480caf1283d6">arm_compute::R</a>, and <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::U8</a>.</p>
-
-<p>Referenced by <a class="el" href="_tensor_library_8cpp_source.xhtml#l00234">TensorLibrary::fill()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> {</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">switch</span>(channel)</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  {</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">case</span> Channel::R:</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">case</span> Channel::G:</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keywordflow">case</span> Channel::B:</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">return</span> Format::U8;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Unsupported channel"</span>);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  }</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> }</div></div><!-- fragment -->
+<div class="fragment"><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> {</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">switch</span>(channel)</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  {</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">case</span> Channel::R:</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">case</span> Channel::G:</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">case</span> Channel::B:</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">return</span> Format::U8;</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">default</span>:</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Unsupported channel"</span>);</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  }</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span> }</div>
+</div><!-- fragment -->
</div>
</div>
<a class="anchor" id="aa337ab76176f3c4193642ac6de3a61cf"></a>
@@ -734,7 +1580,7 @@
<tr>
<td class="memname"><a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> arm_compute::test::get_format_for_channel </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> </td>
+ <td class="paramtype">Channel </td>
<td class="paramname"><em>channel</em></td><td>)</td>
<td></td>
</tr>
@@ -755,12 +1601,108 @@
</dl>
<dl class="section return"><dt>Returns</dt><dd>Format that contains the given channel. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00381">381</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00119">119</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
<p>References <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa9d5ed678fe57bcca610140957afab571">arm_compute::B</a>, <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aadfcf28d0734569a6a693bc8194de62bf">arm_compute::G</a>, <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aae1e1d3d40573127e9ee0480caf1283d6">arm_compute::R</a>, and <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">arm_compute::RGB888</a>.</p>
-<p>Referenced by <a class="el" href="_tensor_library_8cpp_source.xhtml#l00229">TensorLibrary::fill()</a>, and <a class="el" href="_tensor_library_8cpp_source.xhtml#l00450">TensorLibrary::get()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">switch</span>(channel)</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  {</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">case</span> Channel::R:</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordflow">case</span> Channel::G:</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">case</span> Channel::B:</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordflow">return</span> Format::RGB888;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Unsupported channel"</span>);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  }</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> }</div></div><!-- fragment -->
+<p>Referenced by <a class="el" href="_assets_library_8cpp_source.xhtml#l00205">AssetsLibrary::fill()</a>, and <a class="el" href="_assets_library_8cpp_source.xhtml#l00422">AssetsLibrary::get()</a>.</p>
+<div class="fragment"><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>  <span class="keywordflow">switch</span>(channel)</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  {</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordflow">case</span> Channel::R:</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">case</span> Channel::G:</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordflow">case</span> Channel::B:</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">return</span> Format::RGB888;</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">default</span>:</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Unsupported channel"</span>);</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  }</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span> }</div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="ae47155d6186155ec4da9295764b3c05a"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">std::string arm_compute::test::get_typestring </td>
+ <td>(</td>
+ <td class="paramtype">DataType </td>
+ <td class="paramname"><em>data_type</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Obtain numpy type string from DataType. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramdir">[in]</td><td class="paramname">data_type</td><td>Data type.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>numpy type string. </dd></dl>
+
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00445">445</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+
+<p>References <a class="el" href="_error_8h_source.xhtml#l00031">ARM_COMPUTE_ERROR</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::F64</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">arm_compute::S16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::S64</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::S8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">arm_compute::SIZET</a>, <a class="el" href="_toolchain_support_8h_source.xhtml#l00168">arm_compute::support::cpp11::to_string()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">arm_compute::U16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::U32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::U64</a>, and <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::U8</a>.</p>
+
+<p>Referenced by <a class="el" href="_assets_library_8h_source.xhtml#l00653">AssetsLibrary::fill_layer_data()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> {</div>
+<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="comment">// Check endianness</span></div>
+<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1;</div>
+<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> *c = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span><span class="keywordtype">char</span> *<span class="keyword">></span>(&i);</div>
+<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  std::string endianness;</div>
+<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">if</span>(*c == 1)</div>
+<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  {</div>
+<div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  endianness = std::string(<span class="stringliteral">"<"</span>);</div>
+<div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div>
+<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div>
+<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  endianness = std::string(<span class="stringliteral">">"</span>);</div>
+<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  }</div>
+<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keyword">const</span> std::string no_endianness(<span class="stringliteral">"|"</span>);</div>
+<div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div>
+<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="keywordflow">switch</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">data_type</a>)</div>
+<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  {</div>
+<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordflow">case</span> DataType::U8:</div>
+<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">return</span> no_endianness + <span class="stringliteral">"u"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(uint8_t));</div>
+<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keywordflow">case</span> DataType::S8:</div>
+<div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">return</span> no_endianness + <span class="stringliteral">"i"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(int8_t));</div>
+<div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keywordflow">case</span> DataType::U16:</div>
+<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"u"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(uint16_t));</div>
+<div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keywordflow">case</span> DataType::S16:</div>
+<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"i"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(int16_t));</div>
+<div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keywordflow">case</span> DataType::U32:</div>
+<div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"u"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(uint32_t));</div>
+<div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordflow">case</span> DataType::S32:</div>
+<div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"i"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(int32_t));</div>
+<div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordflow">case</span> DataType::U64:</div>
+<div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"u"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(uint64_t));</div>
+<div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordflow">case</span> DataType::S64:</div>
+<div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"i"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(int64_t));</div>
+<div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordflow">case</span> DataType::F32:</div>
+<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"f"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(<span class="keywordtype">float</span>));</div>
+<div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="keywordflow">case</span> DataType::F64:</div>
+<div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"f"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(<span class="keywordtype">double</span>));</div>
+<div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keywordflow">case</span> DataType::SIZET:</div>
+<div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">return</span> endianness + <span class="stringliteral">"u"</span> + <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>(<span class="keyword">sizeof</span>(<span class="keywordtype">size_t</span>));</div>
+<div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordflow">default</span>:</div>
+<div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div>
+<div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  }</div>
+<div class="line"><a name="l00488"></a><span class="lineno"> 488</span> }</div>
+<div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00031">Error.h:31</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_xhtml_ace86dc6f3dfa4f3c256b3999ab250c0a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">arm_compute::test::framework::to_string</a></div><div class="ttdeci">std::string to_string(DatasetMode mode)</div><div class="ttdef"><b>Definition:</b> <a href="_dataset_modes_8h_source.xhtml#l00097">DatasetModes.h:97</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2ad7f431e3446fddcd9b6b9f93c4c14"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00090">MinMaxLocation.cpp:90</a></div></div>
+</div><!-- fragment -->
</div>
</div>
<a class="anchor" id="a24d8c0391cfa38e78969b6ad97c0ff09"></a>
@@ -773,7 +1715,7 @@
<tr>
<td class="memname"><a class="el" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> arm_compute::test::index2coord </td>
<td>(</td>
- <td class="paramtype">const <a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> & </td>
+ <td class="paramtype">const TensorShape & </td>
<td class="paramname"><em>shape</em>, </td>
</tr>
<tr>
@@ -805,16 +1747,34 @@
</dl>
<dl class="section return"><dt>Returns</dt><dd>n-dimensional coordinates. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00611">611</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00308">308</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="_error_8h_source.xhtml#l00115">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00074">TensorShape::set()</a>, and <a class="el" href="_tensor_shape_8h_source.xhtml#l00106">TensorShape::total_size()</a>.</p>
+<p>References <a class="el" href="_error_8h_source.xhtml#l00115">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00074">TensorShape::set()</a>, and <a class="el" href="_tensor_shape_8h_source.xhtml#l00135">TensorShape::total_size()</a>.</p>
-<p>Referenced by <a class="el" href="_tensor_operations_8h_source.xhtml#l00397">arm_compute::test::validation::tensor_operations::box3x3()</a>, <a class="el" href="_tensor_library_8h_source.xhtml#l00401">TensorLibrary::fill()</a>, and <a class="el" href="_validation_8cpp_source.xhtml#l00196">arm_compute::test::validation::validate()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> {</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keywordtype">int</span> num_elements = shape.total_size();</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> </div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(index < 0 || index >= num_elements, <span class="stringliteral">"Index has to be in [0, num_elements]"</span>);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(num_elements == 0, <span class="stringliteral">"Cannot create coordinate from empty shape"</span>);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> </div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  Coordinates coord{ 0 };</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> d = shape.num_dimensions() - 1; d >= 0; --d)</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  {</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  num_elements /= shape[d];</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  coord.set(d, index / num_elements);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  index %= num_elements;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  }</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> </div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordflow">return</span> coord;</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> }</div><div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00115">Error.h:115</a></div></div>
+<p>Referenced by <a class="el" href="_c_p_p_2_box3x3_8cpp_source.xhtml#l00038">arm_compute::test::validation::reference::box3x3()</a>, <a class="el" href="_assets_library_8h_source.xhtml#l00435">AssetsLibrary::fill()</a>, <a class="el" href="_c_p_p_2_gaussian3x3_8cpp_source.xhtml#l00038">arm_compute::test::validation::reference::gaussian3x3()</a>, <a class="el" href="_c_p_p_2_gaussian5x5_8cpp_source.xhtml#l00038">arm_compute::test::validation::reference::gaussian5x5()</a>, <a class="el" href="_c_p_p_2_non_linear_filter_8cpp_source.xhtml#l00036">arm_compute::test::validation::reference::non_linear_filter()</a>, <a class="el" href="_non_maxima_suppression_8cpp_source.xhtml#l00038">arm_compute::test::validation::reference::non_maxima_suppression()</a>, <a class="el" href="_c_p_p_2_scale_8cpp_source.xhtml#l00039">arm_compute::test::validation::reference::scale()</a>, <a class="el" href="_c_p_p_2_sobel_8cpp_source.xhtml#l00106">arm_compute::test::validation::reference::sobel()</a>, <a class="el" href="tests_2validation_2_c_p_p_2_utils_8cpp_source.xhtml#l00069">arm_compute::test::validation::transpose()</a>, and <a class="el" href="_validation_8cpp_source.xhtml#l00173">arm_compute::test::validation::validate()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> {</div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordtype">int</span> num_elements = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.total_size();</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span> </div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(index < 0 || index >= num_elements, <span class="stringliteral">"Index has to be in [0, num_elements]"</span>);</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(num_elements == 0, <span class="stringliteral">"Cannot create coordinate from empty shape"</span>);</div>
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div>
+<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  Coordinates coord{ 0 };</div>
+<div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div>
+<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> d = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.num_dimensions() - 1; d >= 0; --d)</div>
+<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div>
+<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  num_elements /= <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[d];</div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  coord.set(d, index / num_elements);</div>
+<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  index %= num_elements;</div>
+<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  }</div>
+<div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div>
+<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">return</span> coord;</div>
+<div class="line"><a name="l00325"></a><span class="lineno"> 325</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00089">MinMaxLocation.cpp:89</a></div></div>
+<div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00115">Error.h:115</a></div></div>
</div><!-- fragment -->
</div>
</div>
-<a class="anchor" id="a58ee979a599b3b6a2587964106b1910c"></a>
+<a class="anchor" id="a856b55fc20ddcbdbeb84c35ae27bedac"></a>
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@@ -824,13 +1784,13 @@
<tr>
<td class="memname">bool arm_compute::test::is_in_valid_region </td>
<td>(</td>
- <td class="paramtype">const <a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> & </td>
+ <td class="paramtype">const ValidRegion & </td>
<td class="paramname"><em>valid_region</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const <a class="el" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & </td>
+ <td class="paramtype">Coordinates </td>
<td class="paramname"><em>coord</em> </td>
</tr>
<tr>
@@ -848,33 +1808,46 @@
<p>Check if a coordinate is within a valid region. </p>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00658">658</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00355">355</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="_error_8h_source.xhtml#l00115">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00113">ValidRegion::end()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00119">ValidRegion::shape</a>, and <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00107">ValidRegion::start()</a>.</p>
+<p>References <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00118">ValidRegion::end()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00045">Dimensions< int >::num_max_dimensions</a>, and <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00112">ValidRegion::start()</a>.</p>
-<p>Referenced by <a class="el" href="_tensor_operations_8h_source.xhtml#l00397">arm_compute::test::validation::tensor_operations::box3x3()</a>, and <a class="el" href="_validation_8cpp_source.xhtml#l00196">arm_compute::test::validation::validate()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> {</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(valid_region.shape.num_dimensions() != coord.num_dimensions(), <span class="stringliteral">"Shapes of valid region and coordinates do not agree"</span>);</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> d = 0; <span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(d) < coord.num_dimensions(); ++d)</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  {</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keywordflow">if</span>(coord[d] < valid_region.start(d) || coord[d] >= valid_region.end(d))</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  {</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  }</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  }</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> }</div><div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00115">Error.h:115</a></div></div>
+<p>Referenced by <a class="el" href="_c_p_p_2_non_linear_filter_8cpp_source.xhtml#l00036">arm_compute::test::validation::reference::non_linear_filter()</a>, <a class="el" href="_non_maxima_suppression_8cpp_source.xhtml#l00038">arm_compute::test::validation::reference::non_maxima_suppression()</a>, <a class="el" href="_c_p_p_2_sobel_8cpp_source.xhtml#l00106">arm_compute::test::validation::reference::sobel()</a>, and <a class="el" href="_validation_8h_source.xhtml#l00306">arm_compute::test::validation::validate()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> {</div>
+<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> d = 0; d < Coordinates::num_max_dimensions; ++d)</div>
+<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  {</div>
+<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">if</span>(coord[d] < valid_region.start(d) || coord[d] >= valid_region.end(d))</div>
+<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  {</div>
+<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
+<div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  }</div>
+<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  }</div>
+<div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div>
+<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00366"></a><span class="lineno"> 366</span> }</div>
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</div>
-<a class="anchor" id="a356470553f2afd5673a41cf4da48e33b"></a>
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- <tr>
- <td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">int arm_compute::test::required_padding </td>
+ <td class="memname">std::string arm_compute::test::join </td>
<td>(</td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>size</em>, </td>
+ <td class="paramtype">T </td>
+ <td class="paramname"><em>first</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>step</em> </td>
+ <td class="paramtype">T </td>
+ <td class="paramname"><em>last</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const std::string & </td>
+ <td class="paramname"><em>separator</em> </td>
</tr>
<tr>
<td></td>
@@ -882,87 +1855,5395 @@
<td></td><td></td>
</tr>
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- </td>
- <td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">inline</span></span> </td>
- </tr>
-</table>
</div><div class="memdoc">
-<p>Calculate the required padding given the available <code>size</code> and the required. </p>
-<p><code>step</code>.</p>
+<p>Helper function to concatenate multiple strings. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
- <tr><td class="paramdir">[in]</td><td class="paramname">size</td><td>Available size. </td></tr>
- <tr><td class="paramdir">[in]</td><td class="paramname">step</td><td>Required step size.</td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">first</td><td><a class="el" href="classarm__compute_1_1_iterator.xhtml" title="Iterator updated by execute_window_loop for each window element. ">Iterator</a> pointing to the first element to be concatenated. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">last</td><td><a class="el" href="classarm__compute_1_1_iterator.xhtml" title="Iterator updated by execute_window_loop for each window element. ">Iterator</a> pointing behind the last element to be concatenated. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">separator</td><td>String used to join the elements.</td></tr>
</table>
</dd>
</dl>
-<dl class="section return"><dt>Returns</dt><dd>Difference between next greater multiple of <code>step</code> and <code>size</code>. </dd></dl>
+<dl class="section return"><dt>Returns</dt><dd>String containing all elements joined by <code>separator</code>. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00486">486</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00093">93</a> of file <a class="el" href="tests_2framework_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>Referenced by <a class="el" href="tests_2_utils_8h_source.xhtml#l00512">required_padding_undefined_border_read()</a>, and <a class="el" href="tests_2_utils_8h_source.xhtml#l00499">required_padding_undefined_border_write()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> {</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">return</span> ((size + step - 1) / step) * step - size;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> }</div></div><!-- fragment -->
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- <td class="mlabels-left">
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- <td class="memname">int arm_compute::test::required_padding_undefined_border_read </td>
- <td>(</td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>size</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>read_step</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>process_step</em> </td>
- </tr>
- <tr>
- <td></td>
- <td>)</td>
- <td></td><td></td>
- </tr>
- </table>
- </td>
- <td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">inline</span></span> </td>
- </tr>
-</table>
-</div><div class="memdoc">
+<p>References <a class="el" href="accumulate_8cl_source.xhtml#l00041">accumulate()</a>.</p>
-<p>Calculate the required padding for reading operation with UNDEFINED border mode. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramdir">[in]</td><td class="paramname">size</td><td>Available size. </td></tr>
- <tr><td class="paramdir">[in]</td><td class="paramname">read_step</td><td>Required step size; number of elements to read at each iteration. </td></tr>
- <tr><td class="paramdir">[in]</td><td class="paramname">process_step</td><td>Required step size; number of elements to process at each iteration.</td></tr>
- </table>
- </dd>
-</dl>
-<dl class="section return"><dt>Returns</dt><dd>Required padding size. </dd></dl>
-
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00512">512</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-
-<p>References <a class="el" href="tests_2_utils_8h_source.xhtml#l00486">required_padding()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> {</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a356470553f2afd5673a41cf4da48e33b">required_padding</a>(size, process_step) + read_step - process_step;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_xhtml_a356470553f2afd5673a41cf4da48e33b"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a356470553f2afd5673a41cf4da48e33b">arm_compute::test::required_padding</a></div><div class="ttdeci">int required_padding(int size, int step)</div><div class="ttdoc">Calculate the required padding given the available size and the required. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00486">Utils.h:486</a></div></div>
+<p>Referenced by <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00136">join()</a>, and <a class="el" href="_j_s_o_n_printer_8cpp_source.xhtml#l00157">JSONPrinter::print_measurements()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> {</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">return</span> <a class="code" href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">std::accumulate</a>(std::next(first), last, *first, [&separator](<span class="keyword">const</span> std::string & base, <span class="keyword">const</span> std::string & suffix)</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  {</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">return</span> base + separator + suffix;</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  });</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> }</div>
+<div class="ttc" id="accumulate_8cl_xhtml_a00e540076dd545ad59ac7482f8cdf514"><div class="ttname"><a href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">accumulate</a></div><div class="ttdeci">__kernel void accumulate(__global uchar *input_ptr, uint input_stride_x, uint input_step_x, uint input_stride_y, uint input_step_y, uint input_offset_first_element_in_bytes, __global uchar *accu_ptr, uint accu_stride_x, uint accu_step_x, uint accu_stride_y, uint accu_step_y, uint accu_offset_first_element_in_bytes)</div><div class="ttdoc">This function accumulates an input image into output image. </div><div class="ttdef"><b>Definition:</b> <a href="accumulate_8cl_source.xhtml#l00041">accumulate.cl:41</a></div></div>
</div><!-- fragment -->
</div>
</div>
-<a class="anchor" id="a08e86555c8b4d8ae148173d0bda4552f"></a>
+<a class="anchor" id="a898a0423aace06af0f3a18a26a972a1a"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">std::string arm_compute::test::join </td>
+ <td>(</td>
+ <td class="paramtype">T && </td>
+ <td class="paramname"><em>first</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">T && </td>
+ <td class="paramname"><em>last</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const std::string & </td>
+ <td class="paramname"><em>separator</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">UnaryOp && </td>
+ <td class="paramname"><em>op</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Helper function to concatenate multiple values. </p>
+<p>All values are converted to std::string using the provided operation before being joined.</p>
+<p>The signature of op has to be equivalent to std::string op(const T::value_type &val).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramdir">[in]</td><td class="paramname">first</td><td><a class="el" href="classarm__compute_1_1_iterator.xhtml" title="Iterator updated by execute_window_loop for each window element. ">Iterator</a> pointing to the first element to be concatenated. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">last</td><td><a class="el" href="classarm__compute_1_1_iterator.xhtml" title="Iterator updated by execute_window_loop for each window element. ">Iterator</a> pointing behind the last element to be concatenated. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">separator</td><td>String used to join the elements. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">op</td><td>Conversion function.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>String containing all elements joined by <code>separator</code>. </dd></dl>
+
+<p>Definition at line <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00117">117</a> of file <a class="el" href="tests_2framework_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+
+<p>References <a class="el" href="accumulate_8cl_source.xhtml#l00041">accumulate()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> {</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordflow">return</span> <a class="code" href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">std::accumulate</a>(std::next(first), last, op(*first), [&separator, &op](<span class="keyword">const</span> std::string & base, <span class="keyword">const</span> <span class="keyword">typename</span> T::value_type & suffix)</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>  <span class="keywordflow">return</span> base + separator + op(suffix);</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  });</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span> }</div>
+<div class="ttc" id="accumulate_8cl_xhtml_a00e540076dd545ad59ac7482f8cdf514"><div class="ttname"><a href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">accumulate</a></div><div class="ttdeci">__kernel void accumulate(__global uchar *input_ptr, uint input_stride_x, uint input_step_x, uint input_stride_y, uint input_step_y, uint input_offset_first_element_in_bytes, __global uchar *accu_ptr, uint accu_stride_x, uint accu_step_x, uint accu_stride_y, uint accu_step_y, uint accu_offset_first_element_in_bytes)</div><div class="ttdoc">This function accumulates an input image into output image. </div><div class="ttdef"><b>Definition:</b> <a href="accumulate_8cl_source.xhtml#l00041">accumulate.cl:41</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a69835710fc772315f4e65ce156034530"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">std::string arm_compute::test::join </td>
+ <td>(</td>
+ <td class="paramtype">T && </td>
+ <td class="paramname"><em>first</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">T && </td>
+ <td class="paramname"><em>last</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">const std::string & </td>
+ <td class="paramname"><em>separator</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Helper function to concatenate multiple values. </p>
+<p>All values are converted to std::string using std::to_string before being joined.</p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramdir">[in]</td><td class="paramname">first</td><td><a class="el" href="classarm__compute_1_1_iterator.xhtml" title="Iterator updated by execute_window_loop for each window element. ">Iterator</a> pointing to the first element to be concatenated. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">last</td><td><a class="el" href="classarm__compute_1_1_iterator.xhtml" title="Iterator updated by execute_window_loop for each window element. ">Iterator</a> pointing behind the last element to be concatenated. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">separator</td><td>String used to join the elements.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>String containing all elements joined by <code>separator</code>. </dd></dl>
+
+<p>Definition at line <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00136">136</a> of file <a class="el" href="tests_2framework_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+
+<p>References <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00093">join()</a>, and <a class="el" href="_toolchain_support_8h_source.xhtml#l00168">arm_compute::support::cpp11::to_string()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> {</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a69835710fc772315f4e65ce156034530">join</a>(std::forward<T>(first), std::forward<T>(last), separator, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">support::cpp11::to_string</a>);</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_xhtml_ace86dc6f3dfa4f3c256b3999ab250c0a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework.xhtml#ace86dc6f3dfa4f3c256b3999ab250c0a">arm_compute::test::framework::to_string</a></div><div class="ttdeci">std::string to_string(DatasetMode mode)</div><div class="ttdef"><b>Definition:</b> <a href="_dataset_modes_8h_source.xhtml#l00097">DatasetModes.h:97</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a69835710fc772315f4e65ce156034530"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a69835710fc772315f4e65ce156034530">arm_compute::test::join</a></div><div class="ttdeci">std::string join(T &&first, T &&last, const std::string &separator)</div><div class="ttdoc">Helper function to concatenate multiple values. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2framework_2_utils_8h_source.xhtml#l00136">Utils.h:136</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="ad7d919409d3d679cfbf28b2dae757fec"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">MobileNetDepthwiseConvolution </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLDepthwiseConvolutionFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::MobileNetDepthwiseConvolutionDataset(), data_types), framework::dataset::make("Batches",{1}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1f4b9eae17da2aebc223b0fdeee74cea"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">MobileNetDepthwiseSeparableConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLDepthwiseSeparableConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::MobileNetDepthwiseSeparableConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa14390b7bed93ce327f5dedd89fc8928"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SmallROIPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEROIPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SmallROIPoolingLayerDataset(), framework::dataset::make("DataType",{DataType::F32})), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac7d54f1a842ebb07f378846c21ccbe97"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SmallROIPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLROIPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SmallROIPoolingLayerDataset(), framework::dataset::make("DataType",{DataType::F16, DataType::F32})), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6faf0b684dd2c7e5bb111dd8f8f8c6f1"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">Floor </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFloorFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::SmallShapes(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6e81878e7ca8fecdd9f6e6bcc2a7b794"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">Floor </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFloorFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::SmallShapes(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2fba44656470195a6245f922a1c264f5"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2b1950a60a98dec32a7ca7531fded8ad"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1GEMM </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLGEMMFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::GoogLeNetInceptionV1GEMMDataset(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a70d28ab3b5936a6454451d42f3c170f3"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetNormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aef76c7b3cf7d1e3c6fc7ff96804a5753"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">MatrixMultiplyGEMM </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLGEMMFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::MatrixMultiplyGEMMDataset(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af935c08091163839aead6ac3023c2147"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
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+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLGEMMFixture </td>
+ <td class="paramname">, </td>
+ </tr>
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+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::GoogleNetGEMMDataset(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
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+ <td class="paramtype">AlexNetNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NENormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetNormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <td class="paramtype">AlexNetFullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetFullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="namespacearm__compute_1_1test.xhtml#a6a292ad5fedcc7dea6c6eb1be6d4c0d3">NELeNet5Fixture</a> </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>make</em>"Batches",{1, 4, 8} </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ <td class="paramtype">AlexNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="namespacearm__compute_1_1test.xhtml#ae3b678c8477dd5acc5e264eae37b562c">CLLeNet5Fixture</a> </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>make</em>"Batches",{1, 4, 8} </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1NormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1NormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetFullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetFullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1GEMM </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEGEMMFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::GoogLeNetInceptionV1GEMMDataset(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1NormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NENormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1NormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetActivationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td>(</td>
+ <td class="paramtype">AlexNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ </table>
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+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEGEMMFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::MatrixMultiplyGEMMDataset(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td>(</td>
+ <td class="paramtype">AlexNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <td>(</td>
+ <td class="paramtype">LeNet5PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <td>(</td>
+ <td class="paramtype">GoogleNetGEMM </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEGEMMFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>datasets::GoogleNetGEMMDataset(), data_types </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetNormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNet </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLAlexNetFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::make("DataType",{DataType::F16, DataType::F32}), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5ActivationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa0a358cbff96894b77c9b3cfba3c2db4"></a>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NENormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetNormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1NormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1NormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1ActivationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4BatchNormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEBatchNormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4BatchNormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1NormalizationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NENormalizationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1NormalizationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNet </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEAlexNetFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>alex_net_data_types, framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
+<a class="anchor" id="aecd85eec5df288174be9b7e0fac6d1fe"></a>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
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+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
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+ <td></td>
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+ <td>(</td>
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+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4ActivationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td>(</td>
+ <td class="paramtype">SqueezeNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ <td class="paramtype">SqueezeNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetActivationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16ActivationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetFullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetFullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac62a5389a4a60e89fabb6bb2153adfc5"></a>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
+<a class="anchor" id="a8050efac909e6e8fce5791d2205fe0a8"></a>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a572b94c09ce496eda95d8d544dc1c4d1"></a>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2ActivationLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
+<a class="anchor" id="a4325316dca63988d0c63c8e761143557"></a>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetFullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetFullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+</div>
+</div>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="adb9a698039f2f9414f3296a4d9070893"></a>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a11c4e187683f0687472d48d8f279c8fc"></a>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a27446bd5b343d26d6028cd2ab34065a6"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abb38304d29f99717ecc5c528962972a5"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a36cad137a713f8be3263a1a6466c6bd7"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::ALL </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2PoolingLayerDataset(), data_types), framework::dataset::make("Batches", 1) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a65a028ab7f8ba81db43d5963ea5343a4"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad5101e30d9b5306231c7ed2ce71f350b"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a13170587db62e123a041d2b8cab82ef8"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
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+ <td class="paramtype">NEConvolutionLayerFixture </td>
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+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
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+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
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+ <tr>
+ <td></td>
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+ <td>(</td>
+ <td class="paramtype">LeNet5FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td>(</td>
+ <td class="paramtype">AlexNetActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetActivationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetDirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">AlexNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::AlexNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5ActivationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1ActivationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
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+</div><div class="memdoc">
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+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9adba78f24e5c87b2c95a1c5e23883e9"></a>
+<div class="memitem">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a731977f1de2e0d6dd1512818540ab608"></a>
+<div class="memitem">
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+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">LeNet5PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::LeNet5PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a029d80ad64be335749e827cc64efd88c"></a>
+<div class="memitem">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+<div class="memitem">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad5d101b25bb35a8d9b1efc70102bb3a4"></a>
+<div class="memitem">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a03474ce6764bea95de0edb583d281017"></a>
+<div class="memitem">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aabdb95f3f541376f38e03d63957cd0af"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2DirectConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6ada452bc1053385b8574f38d341ffc9"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4ActivationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a34df6fb97233366fc9083d79c13a5737"></a>
+<div class="memitem">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a967825a64c529b573ca62e74179ee921"></a>
+<div class="memitem">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV1PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV1PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a68166bcb788035f5a6c17fe0c68ae730"></a>
+<div class="memitem">
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+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1d77d86fcdca1b8578756eae70fcac85"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4FullyConnectedLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEFullyConnectedLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4FullyConnectedLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6692a58c12e2eff315715e6c971d0230"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetActivationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0ca04d4de125be45c16b579b43d53835"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac06bd6612edf1bbb0c0f4b0d4aa86b32"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">GoogLeNetInceptionV4PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::GoogLeNetInceptionV4PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5a371e1a37be130dc9e8c905cd5efc29"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a26e3678291b5f879d82808eda0d39bc2"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16ActivationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5f97a3f0575116d348f47489487d4214"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">SqueezeNetPoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::SqueezeNetPoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7473924d4fdf2b5dec0d8ee9aa11e25d"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2ConvolutionLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLConvolutionLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), data_types), framework::dataset::make("Batches",{1, 4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab77581768cf2f7433ba92c2b42c4617e"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2ActivationLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">NEActivationLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2ActivationLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6a51ef57457c994f04d0b54e76387add"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">VGG16PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::VGG16PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7d579c9d463693975486ea2248adc966"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE </td>
+ <td>(</td>
+ <td class="paramtype">YOLOV2PoolingLayer </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">CLPoolingLayerFixture </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::DatasetMode::NIGHTLY </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">framework::dataset:: </td>
+ <td class="paramname"><em>combine</em>framework::dataset::combine(datasets::YOLOV2PoolingLayerDataset(), data_types), framework::dataset::make("Batches",{4, 8}) </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad93bb148a873f19ad7692756e59617f4"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -970,22 +7251,16 @@
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">int arm_compute::test::required_padding_undefined_border_write </td>
+ <td class="memname">T arm_compute::test::round_half_even </td>
<td>(</td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>size</em>, </td>
+ <td class="paramtype">T </td>
+ <td class="paramname"><em>value</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>step</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>border_size</em> </td>
+ <td class="paramtype">T </td>
+ <td class="paramname"><em>epsilon</em> = <code>std::numeric_limits<T>::epsilon()</code> </td>
</tr>
<tr>
<td></td>
@@ -1000,21 +7275,80 @@
</table>
</div><div class="memdoc">
-<p>Calculate the required padding for writing operation with UNDEFINED border mode. </p>
+<p>Round floating-point value with half value rounding to nearest even. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
- <tr><td class="paramdir">[in]</td><td class="paramname">size</td><td>Available size. </td></tr>
- <tr><td class="paramdir">[in]</td><td class="paramname">step</td><td>Required step size; number of elements to write at each iteration. </td></tr>
- <tr><td class="paramdir">[in]</td><td class="paramname">border_size</td><td>Border size.</td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">value</td><td>floating-point value to be rounded. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">epsilon</td><td>precision.</td></tr>
</table>
</dd>
</dl>
-<dl class="section return"><dt>Returns</dt><dd>Required padding size plus border size. </dd></dl>
+<dl class="section return"><dt>Returns</dt><dd>Floating-point value of rounded <code>value</code>. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00499">499</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00069">69</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="tests_2_utils_8h_source.xhtml#l00486">required_padding()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> {</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a356470553f2afd5673a41cf4da48e33b">required_padding</a>(size, step) + border_size;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_xhtml_a356470553f2afd5673a41cf4da48e33b"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a356470553f2afd5673a41cf4da48e33b">arm_compute::test::required_padding</a></div><div class="ttdeci">int required_padding(int size, int step)</div><div class="ttdoc">Calculate the required padding given the available size and the required. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00486">Utils.h:486</a></div></div>
+<p>References <a class="el" href="tests_2validation_2_fixed_point_8h_source.xhtml#l00914">arm_compute::test::fixed_point_arithmetic::detail::abs()</a>, <a class="el" href="_toolchain_support_8h_source.xhtml#l00259">arm_compute::support::cpp11::copysign()</a>, and <a class="el" href="_toolchain_support_8h_source.xhtml#l00228">arm_compute::support::cpp11::round()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  T positive_value = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ae115fc750a92fb6a5e094998b56fcc56">std::abs</a>(<a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>);</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  T ipart = 0;</div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  std::modf(positive_value, &ipart);</div>
+<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// If 'value' is exactly halfway between two integers</span></div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ae115fc750a92fb6a5e094998b56fcc56">std::abs</a>(positive_value - (ipart + 0.5f)) < epsilon)</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  {</div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">// If 'ipart' is even then return 'ipart'</span></div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">if</span>(std::fmod(ipart, 2.f) < epsilon)</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  {</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a28096f8372c0ad762864c790917375e2">support::cpp11::copysign</a>(ipart, <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>);</div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">// Else return the nearest even integer</span></div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a28096f8372c0ad762864c790917375e2">support::cpp11::copysign</a>(std::ceil(ipart + 0.5f), <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>);</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  }</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="comment">// Otherwise use the usual round to closest</span></div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a28096f8372c0ad762864c790917375e2">support::cpp11::copysign</a>(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#ab71c35ca207b916a9f8b0336ab88484e">support::cpp11::round</a>(positive_value), <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>);</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1support_1_1cpp11_xhtml_a28096f8372c0ad762864c790917375e2"><div class="ttname"><a href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a28096f8372c0ad762864c790917375e2">arm_compute::support::cpp11::copysign</a></div><div class="ttdeci">T copysign(T x, T y)</div><div class="ttdoc">Composes a floating point value with the magnitude of x and the sign of y. </div><div class="ttdef"><b>Definition:</b> <a href="_toolchain_support_8h_source.xhtml#l00259">ToolchainSupport.h:259</a></div></div>
+<div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00269">hwc.hpp:269</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1support_1_1cpp11_xhtml_ab71c35ca207b916a9f8b0336ab88484e"><div class="ttname"><a href="namespacearm__compute_1_1support_1_1cpp11.xhtml#ab71c35ca207b916a9f8b0336ab88484e">arm_compute::support::cpp11::round</a></div><div class="ttdeci">T round(T value)</div><div class="ttdoc">Round floating-point value with half value rounding away from zero. </div><div class="ttdef"><b>Definition:</b> <a href="_toolchain_support_8h_source.xhtml#l00228">ToolchainSupport.h:228</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ae115fc750a92fb6a5e094998b56fcc56"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ae115fc750a92fb6a5e094998b56fcc56">arm_compute::test::fixed_point_arithmetic::detail::abs</a></div><div class="ttdeci">fixed_point< T > abs(fixed_point< T > x)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00914">FixedPoint.h:914</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="af4bcf30f8c56f547f66d61c7c5ae01db"></a>
+<div class="memitem">
+<div class="memproto">
+<table class="mlabels">
+ <tr>
+ <td class="mlabels-left">
+ <table class="memname">
+ <tr>
+ <td class="memname">T arm_compute::test::round_half_up </td>
+ <td>(</td>
+ <td class="paramtype">T </td>
+ <td class="paramname"><em>value</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Round floating-point value with half value rounding to positive infinity. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramdir">[in]</td><td class="paramname">value</td><td>floating-point value to be rounded.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Floating-point value of rounded <code>value</code>. </dd></dl>
+
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00056">56</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<div class="fragment"><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>  <span class="keywordflow">return</span> std::floor(<a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> + 0.5f);</div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> }</div>
+<div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00269">hwc.hpp:269</a></div></div>
</div><!-- fragment -->
</div>
</div>
@@ -1041,16 +7375,27 @@
</dl>
<dl class="section return"><dt>Returns</dt><dd>saturated value. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00581">581</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00278">278</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="tests_2validation_2_fixed_point_8h_source.xhtml#l00880">arm_compute::test::fixed_point_arithmetic::detail::max()</a>.</p>
+<p>References <a class="el" href="tests_2validation_2_fixed_point_8h_source.xhtml#l00889">arm_compute::test::fixed_point_arithmetic::detail::max()</a>.</p>
-<p>Referenced by <a class="el" href="_tensor_operations_8h_source.xhtml#l00270">arm_compute::test::validation::tensor_operations::absolute_difference()</a>, <a class="el" href="_tensor_operations_8h_source.xhtml#l00283">arm_compute::test::validation::tensor_operations::accumulate()</a>, and <a class="el" href="_tensor_operations_8h_source.xhtml#l00296">arm_compute::test::validation::tensor_operations::accumulate_squared()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> {</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keywordflow">if</span>(val > static_cast<T>(<a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<U>::max</a>()))</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  {</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  val = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(<a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<U>::max</a>());</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  }</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keywordflow">if</span>(val < static_cast<T>(std::numeric_limits<U>::lowest()))</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  {</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  val = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(std::numeric_limits<U>::lowest());</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  }</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="keywordflow">return</span> val;</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00880">FixedPoint.h:880</a></div></div>
+<p>Referenced by <a class="el" href="validation_2_c_p_p_2_depthwise_convolution_8cpp_source.xhtml#l00048">arm_compute::test::validation::reference::depthwise_convolution()</a>, and <a class="el" href="_c_p_p_2_non_linear_filter_8cpp_source.xhtml#l00036">arm_compute::test::validation::reference::non_linear_filter()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> {</div>
+<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keywordflow">if</span>(val > static_cast<T>(<a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<U>::max</a>()))</div>
+<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  {</div>
+<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  val = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(<a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<U>::max</a>());</div>
+<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  }</div>
+<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">if</span>(val < static_cast<T>(std::numeric_limits<U>::lowest()))</div>
+<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {</div>
+<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  val = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(std::numeric_limits<U>::lowest());</div>
+<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  }</div>
+<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordflow">return</span> val;</div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00889">FixedPoint.h:889</a></div></div>
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</div>
</div>
-<a class="anchor" id="a670cba074b4be0bf9af03e48250bd616"></a>
+<a class="anchor" id="a4c9ad143c34306817986409ffb1dbd40"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -1060,53 +7405,20 @@
<tr>
<td class="memname"><a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> arm_compute::test::shape_to_valid_region </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> </td>
- <td class="paramname"><em>shape</em></td><td>)</td>
- <td></td>
- </tr>
- </table>
- </td>
- <td class="mlabels-right">
-<span class="mlabels"><span class="mlabel">inline</span></span> </td>
- </tr>
-</table>
-</div><div class="memdoc">
-
-<p>Create a valid region covering the enitre tensor shape. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramdir">[in]</td><td class="paramname">shape</td><td>Shape used as size of the valid region.</td></tr>
- </table>
- </dd>
-</dl>
-<dl class="section return"><dt>Returns</dt><dd>A valid region starting at (0, 0, ...) with size of <code>shape</code>. </dd></dl>
-
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00452">452</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-
-<p>References <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00074">Dimensions< T >::set()</a>.</p>
-
-<p>Referenced by <a class="el" href="_validation_8cpp_source.xhtml#l00190">arm_compute::test::validation::validate()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> {</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  Coordinates anchor;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  anchor.set(std::max<int>(0, shape.num_dimensions() - 1), 0);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordflow">return</span> ValidRegion(std::move(anchor), std::move(shape));</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> }</div></div><!-- fragment -->
-</div>
-</div>
-<a class="anchor" id="a6b97d7bba7b5cee833eb5c2282e8d246"></a>
-<div class="memitem">
-<div class="memproto">
-<table class="mlabels">
- <tr>
- <td class="mlabels-left">
- <table class="memname">
- <tr>
- <td class="memname"><a class="el" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> arm_compute::test::shape_to_valid_region_undefined_border </td>
- <td>(</td>
- <td class="paramtype"><a class="el" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> </td>
+ <td class="paramtype">TensorShape </td>
<td class="paramname"><em>shape</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a> </td>
- <td class="paramname"><em>border_size</em> </td>
+ <td class="paramtype">bool </td>
+ <td class="paramname"><em>border_undefined</em> = <code>false</code>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">BorderSize </td>
+ <td class="paramname"><em>border_size</em> = <code>BorderSize(0)</code> </td>
</tr>
<tr>
<td></td>
@@ -1121,22 +7433,45 @@
</table>
</div><div class="memdoc">
-<p>Create a valid region covering the tensor shape with UNDEFINED border mode and specified border size. </p>
+<p>Create a valid region based on tensor shape, border mode and border size. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">shape</td><td>Shape used as size of the valid region. </td></tr>
- <tr><td class="paramdir">[in]</td><td class="paramname">border_size</td><td>Border size used to specify the region to exclude.</td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">border_undefined</td><td>(Optional) Boolean indicating if the border mode is undefined. </td></tr>
+ <tr><td class="paramdir">[in]</td><td class="paramname">border_size</td><td>(Optional) Border size used to specify the region to exclude.</td></tr>
</table>
</dd>
</dl>
-<dl class="section return"><dt>Returns</dt><dd>A valid region starting at (<code>border_size.left</code>, <code>border_size.top</code>, ...) with reduced size of <code>shape</code>. </dd></dl>
+<dl class="section return"><dt>Returns</dt><dd>A valid region starting at (0, 0, ...) with size of <code>shape</code> if <code>border_undefined</code> is false; otherwise return A valid region starting at (<code>border_size.left</code>, <code>border_size.top</code>, ...) with reduced size of <code>shape</code>. </dd></dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00466">466</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00193">193</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="_error_8h_source.xhtml#l00124">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00197">BorderSize::bottom</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00198">BorderSize::left</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00196">BorderSize::right</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00074">TensorShape::set()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00074">Dimensions< T >::set()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00195">BorderSize::top</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions< T >::x()</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00086">Dimensions< T >::y()</a>.</p>
+<p>References <a class="el" href="_error_8h_source.xhtml#l00124">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="tests_2validation_2_fixed_point_8h_source.xhtml#l00889">arm_compute::test::fixed_point_arithmetic::detail::max()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00109">Dimensions< T >::num_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00074">TensorShape::set()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00074">Dimensions< T >::set()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00115">Dimensions< T >::set_num_dimensions()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions< T >::x()</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00086">Dimensions< T >::y()</a>.</p>
-<p>Referenced by <a class="el" href="_tensor_operations_8h_source.xhtml#l00397">arm_compute::test::validation::tensor_operations::box3x3()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(shape.num_dimensions() < 2);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  Coordinates anchor;</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  anchor.set(std::max<int>(0, shape.num_dimensions() - 1), 0);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  anchor.set(0, border_size.left);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  anchor.set(1, border_size.top);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  shape.set(0, shape.x() - border_size.left - border_size.right);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  shape.set(1, shape.y() - border_size.top - border_size.bottom);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordflow">return</span> ValidRegion(std::move(anchor), shape);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> }</div><div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00124">Error.h:124</a></div></div>
+<p>Referenced by <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00064">arm_compute::test::validation::DATA_TEST_CASE()</a>, <a class="el" href="_c_l_2_box3x3_8cpp_source.xhtml#l00091">arm_compute::test::validation::FIXTURE_DATA_TEST_CASE()</a>, <a class="el" href="_c_p_p_2_non_linear_filter_8cpp_source.xhtml#l00036">arm_compute::test::validation::reference::non_linear_filter()</a>, <a class="el" href="_non_maxima_suppression_8cpp_source.xhtml#l00038">arm_compute::test::validation::reference::non_maxima_suppression()</a>, <a class="el" href="_c_p_p_2_sobel_8cpp_source.xhtml#l00106">arm_compute::test::validation::reference::sobel()</a>, and <a class="el" href="_validation_8h_source.xhtml#l00299">arm_compute::test::validation::validate()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> {</div>
+<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  Coordinates anchor;</div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  anchor.set_num_dimensions(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.num_dimensions());</div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">if</span>(border_undefined)</div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.num_dimensions() < 2);</div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div>
+<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  anchor.set(0, border_size.left);</div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  anchor.set(1, border_size.top);</div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> valid_shape_x = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::max</a>(0, static_cast<int>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.x()) - static_cast<int>(border_size.left) - <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(border_size.right));</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> valid_shape_y = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::max</a>(0, static_cast<int>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.y()) - static_cast<int>(border_size.top) - <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(border_size.bottom));</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.set(0, valid_shape_x);</div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.set(1, valid_shape_y);</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  }</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">return</span> ValidRegion(std::move(anchor), std::move(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>));</div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00089">MinMaxLocation.cpp:89</a></div></div>
+<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00124">Error.h:124</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00889">FixedPoint.h:889</a></div></div>
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@@ -1159,7 +7494,7 @@
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<td></td>
- <td class="paramtype"><a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> </td>
+ <td class="paramtype">DataType </td>
<td class="paramname"><em>data_type</em> </td>
</tr>
<tr>
@@ -1181,29 +7516,77 @@
</dd>
</dl>
-<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00526">526</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+<p>Definition at line <a class="el" href="tests_2_utils_8h_source.xhtml#l00224">224</a> of file <a class="el" href="tests_2_utils_8h_source.xhtml">Utils.h</a>.</p>
-<p>References <a class="el" href="_error_8h_source.xhtml#l00031">ARM_COMPUTE_ERROR</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::F64</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">arm_compute::QS8</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">arm_compute::S16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::S64</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::S8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">arm_compute::SIZET</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">arm_compute::U16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::U32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::U64</a>, and <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::U8</a>.</p>
+<p>References <a class="el" href="_error_8h_source.xhtml#l00031">ARM_COMPUTE_ERROR</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::F64</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">arm_compute::QS16</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">arm_compute::QS8</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">arm_compute::S16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::S64</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::S8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">arm_compute::SIZET</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">arm_compute::U16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::U32</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::U64</a>, and <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::U8</a>.</p>
-<p>Referenced by <a class="el" href="_tensor_library_8h_source.xhtml#l00368">TensorLibrary::fill()</a>, and <a class="el" href="_tensor_library_8h_source.xhtml#l00621">TensorLibrary::fill_layer_data()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> {</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keywordflow">switch</span>(data_type)</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  {</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keywordflow">case</span> DataType::U8:</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  *<span class="keyword">reinterpret_cast<</span>uint8_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keywordflow">case</span> DataType::S8:</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordflow">case</span> DataType::QS8:</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  *<span class="keyword">reinterpret_cast<</span>int8_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">case</span> DataType::U16:</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  *<span class="keyword">reinterpret_cast<</span>uint16_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="keywordflow">case</span> DataType::S16:</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  *<span class="keyword">reinterpret_cast<</span>int16_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordflow">case</span> DataType::U32:</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  *<span class="keyword">reinterpret_cast<</span>uint32_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keywordflow">case</span> DataType::S32:</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  *<span class="keyword">reinterpret_cast<</span>int32_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keywordflow">case</span> DataType::U64:</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  *<span class="keyword">reinterpret_cast<</span>uint64_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keywordflow">case</span> DataType::S64:</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  *<span class="keyword">reinterpret_cast<</span>int64_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> <span class="preprocessor">#ifdef ENABLE_FP16</span></div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordflow">case</span> DataType::F16:</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  *<span class="keyword">reinterpret_cast<</span>float16_t *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> <span class="preprocessor">#endif </span><span class="comment">/* ENABLE_FP16 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">case</span> DataType::F32:</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="keywordflow">case</span> DataType::F64:</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">double</span> *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="keywordflow">case</span> DataType::SIZET:</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">size_t</span> *<span class="keyword">></span>(ptr) = value;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  }</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> }</div><div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00031">Error.h:31</a></div></div>
+<p>Referenced by <a class="el" href="_assets_library_8h_source.xhtml#l00400">AssetsLibrary::fill()</a>, and <a class="el" href="_assets_library_8h_source.xhtml#l00374">AssetsLibrary::fill_borders_with_garbage()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> {</div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">switch</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">data_type</a>)</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  {</div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">case</span> DataType::U8:</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  *<span class="keyword">reinterpret_cast<</span>uint8_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">case</span> DataType::S8:</div>
+<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">case</span> DataType::QS8:</div>
+<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  *<span class="keyword">reinterpret_cast<</span>int8_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">case</span> DataType::U16:</div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  *<span class="keyword">reinterpret_cast<</span>uint16_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">case</span> DataType::S16:</div>
+<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordflow">case</span> DataType::QS16:</div>
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  *<span class="keyword">reinterpret_cast<</span>int16_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">case</span> DataType::U32:</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  *<span class="keyword">reinterpret_cast<</span>uint32_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">case</span> DataType::S32:</div>
+<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  *<span class="keyword">reinterpret_cast<</span>int32_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">case</span> DataType::U64:</div>
+<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  *<span class="keyword">reinterpret_cast<</span>uint64_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">case</span> DataType::S64:</div>
+<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  *<span class="keyword">reinterpret_cast<</span>int64_t *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">case</span> DataType::F16:</div>
+<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  *<span class="keyword">reinterpret_cast<</span><a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordflow">case</span> DataType::F32:</div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordflow">case</span> DataType::F64:</div>
+<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">double</span> *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">case</span> DataType::SIZET:</div>
+<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">size_t</span> *<span class="keyword">></span>(ptr) = <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div>
+<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">default</span>:</div>
+<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div>
+<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  }</div>
+<div class="line"><a name="l00269"></a><span class="lineno"> 269</span> }</div>
+<div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00031">Error.h:31</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a73e2825fd61d349c5ca2f5313e3c8ea1"><div class="ttname"><a href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">arm_compute::half</a></div><div class="ttdeci">half_float::half half</div><div class="ttdoc">16-bit floating point type </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00039">Types.h:39</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2ad7f431e3446fddcd9b6b9f93c4c14"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_min_max_location_8cpp_source.xhtml#l00090">MinMaxLocation.cpp:90</a></div></div>
+<div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00269">hwc.hpp:269</a></div></div>
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+<a class="anchor" id="a28edc8880596d14c099f3c2509efc8b3"></a>
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<td class="memname">void arm_compute::test::swap </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> & </td>
+ <td class="paramtype">SimpleTensor< U > & </td>
<td class="paramname"><em>tensor1</em>, </td>
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<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> & </td>
+ <td class="paramtype">SimpleTensor< U > & </td>
<td class="paramname"><em>tensor2</em> </td>
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-<p>Definition at line <a class="el" href="_raw_tensor_8cpp_source.xhtml#l00168">168</a> of file <a class="el" href="_raw_tensor_8cpp_source.xhtml">RawTensor.cpp</a>.</p>
+<p>Definition at line <a class="el" href="_simple_tensor_8h_source.xhtml#l00335">335</a> of file <a class="el" href="_simple_tensor_8h_source.xhtml">SimpleTensor.h</a>.</p>
-<p>References <a class="el" href="_raw_tensor_8cpp_source.xhtml#l00168">RawTensor::swap</a>.</p>
-<div class="fragment"><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// Use unqualified call to swap to enable ADL. But make std::swap available</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">// as backup.</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">using</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">std::swap</a>;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">swap</a>(tensor1._shape, tensor2._shape);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">swap</a>(tensor1._format, tensor2._format);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">swap</a>(tensor1._data_type, tensor2._data_type);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">swap</a>(tensor1._num_channels, tensor2._num_channels);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">swap</a>(tensor1._buffer, tensor2._buffer);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_xhtml_ac04d36ea1fb41a3dfd3afd4a89ef2470"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#ac04d36ea1fb41a3dfd3afd4a89ef2470">arm_compute::test::swap</a></div><div class="ttdeci">void swap(RawTensor &tensor1, RawTensor &tensor2)</div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00168">RawTensor.cpp:168</a></div></div>
+<p>Referenced by <a class="el" href="_simple_tensor_8h_source.xhtml#l00214">SimpleTensor< T >::operator=()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> {</div>
+<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="comment">// Use unqualified call to swap to enable ADL. But make std::swap available</span></div>
+<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="comment">// as backup.</span></div>
+<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">using</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">std::swap</a>;</div>
+<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">swap</a>(tensor1._shape, tensor2._shape);</div>
+<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">swap</a>(tensor1._format, tensor2._format);</div>
+<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">swap</a>(tensor1._data_type, tensor2._data_type);</div>
+<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">swap</a>(tensor1._num_channels, tensor2._num_channels);</div>
+<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">swap</a>(tensor1._buffer, tensor2._buffer);</div>
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a28edc8880596d14c099f3c2509efc8b3"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">arm_compute::test::swap</a></div><div class="ttdeci">void swap(SimpleTensor< U > &tensor1, SimpleTensor< U > &tensor2)</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00335">SimpleTensor.h:335</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a class="anchor" id="a5b67cbf475b1e1d3bec9b0b937fdafac"></a>
+<div class="memitem">
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+ <td class="mlabels-left">
+ <table class="memname">
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+ <td class="memname">std::string arm_compute::test::tolower </td>
+ <td>(</td>
+ <td class="paramtype">std::string </td>
+ <td class="paramname"><em>string</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+ </td>
+ <td class="mlabels-right">
+<span class="mlabels"><span class="mlabel">inline</span></span> </td>
+ </tr>
+</table>
+</div><div class="memdoc">
+
+<p>Convert string to lower case. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramdir">[in]</td><td class="paramname">string</td><td>To be converted string.</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Lower case string. </dd></dl>
+
+<p>Definition at line <a class="el" href="tests_2framework_2_utils_8h_source.xhtml#l00147">147</a> of file <a class="el" href="tests_2framework_2_utils_8h_source.xhtml">Utils.h</a>.</p>
+
+<p>Referenced by <a class="el" href="_dataset_modes_8cpp_source.xhtml#l00036">arm_compute::test::framework::dataset_mode_from_name()</a>, <a class="el" href="_instruments_8cpp_source.xhtml#l00037">arm_compute::test::framework::instrument_type_from_name()</a>, <a class="el" href="_printers_8cpp_source.xhtml#l00037">arm_compute::test::framework::log_format_from_name()</a>, and <a class="el" href="_exceptions_8cpp_source.xhtml#l00037">arm_compute::test::framework::log_level_from_name()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> {</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  std::transform(<span class="keywordtype">string</span>.begin(), <span class="keywordtype">string</span>.end(), <span class="keywordtype">string</span>.begin(), [](<span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> c)</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="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a5b67cbf475b1e1d3bec9b0b937fdafac">std::tolower</a>(c);</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  });</div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">return</span> string;</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span> }</div>
+<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a5b67cbf475b1e1d3bec9b0b937fdafac"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a5b67cbf475b1e1d3bec9b0b937fdafac">arm_compute::test::tolower</a></div><div class="ttdeci">std::string tolower(std::string string)</div><div class="ttdoc">Convert string to lower case. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2framework_2_utils_8h_source.xhtml#l00147">Utils.h:147</a></div></div>
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<h2 class="groupheader">Variable Documentation</h2>
-<a class="anchor" id="a4ced6442a379a75e8a6c4be093fb666b"></a>
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- <td class="memname">std::unique_ptr< <a class="el" href="classarm__compute_1_1test_1_1_tensor_library.xhtml">TensorLibrary</a> > library</td>
+ <td class="memname">const auto data_types = <a class="el" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>("DataType", { DataType::F32 })</td>
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-<p>Definition at line <a class="el" href="benchmark_2main_8cpp_source.xhtml#l00050">50</a> of file <a class="el" href="benchmark_2main_8cpp_source.xhtml">main.cpp</a>.</p>
-
-<p>Referenced by <a class="el" href="_reference_8cpp_source.xhtml#l00056">Reference::compute_reference_absolute_difference()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00073">Reference::compute_reference_accumulate()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00089">Reference::compute_reference_accumulate_squared()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00106">Reference::compute_reference_accumulate_weighted()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00331">Reference::compute_reference_activation_layer()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00122">Reference::compute_reference_arithmetic_addition()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00139">Reference::compute_reference_arithmetic_subtraction()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00361">Reference::compute_reference_batch_normalization_layer()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00156">Reference::compute_reference_bitwise_and()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00207">Reference::compute_reference_bitwise_not()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00173">Reference::compute_reference_bitwise_or()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00190">Reference::compute_reference_bitwise_xor()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00222">Reference::compute_reference_box3x3()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00405">Reference::compute_reference_convolution_layer()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00237">Reference::compute_reference_depth_convert()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00555">Reference::compute_reference_fixed_point_operation()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00298">Reference::compute_reference_fixed_point_pixel_wise_multiplication()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00435">Reference::compute_reference_fully_connected_layer()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00251">Reference::compute_reference_gemm()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00042">Reference::compute_reference_integral_image()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00475">Reference::compute_reference_normalization_layer()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00280">Reference::compute_reference_pixel_wise_multiplication()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00499">Reference::compute_reference_pooling_layer()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00530">Reference::compute_reference_softmax_layer()</a>, <a class="el" href="_reference_8cpp_source.xhtml#l00316">Reference::compute_reference_threshold()</a>, <a class="el" href="_c_l_2_helper_8h_source.xhtml#l00062">arm_compute::test::cl::create_tensor()</a>, <a class="el" href="_n_e_o_n_2_helper_8h_source.xhtml#l00063">arm_compute::test::neon::create_tensor()</a>, <a class="el" href="model__objects_2_le_net5_8h_source.xhtml#l00135">LeNet5< TensorType, Accessor, ActivationLayerFunction, ConvolutionLayerFunction, FullyConnectedLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction >::fill_random()</a>, <a class="el" href="model__objects_2_alex_net_8h_source.xhtml#l00338">AlexNet< ITensorType, TensorType, SubTensorType, Accessor, ActivationLayerFunction, ConvolutionLayerFunction, FullyConnectedLayerFunction, NormalizationLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction, dt >::fill_random()</a>, <a class="el" href="benchmark_2main_8cpp_source.xhtml#l00054">main()</a>, <a class="el" href="_activation_layer_8h_source.xhtml#l00047">ActivationLayer< DataSet, TensorType, Accessor, Function, dt >::SetUp()</a>, <a class="el" href="_convolution_layer_8h_source.xhtml#l00047">ConvolutionLayer< DataSet, TensorType, Accessor, Function, dt >::SetUp()</a>, <a class="el" href="_pooling_layer_8h_source.xhtml#l00047">PoolingLayer< DataSet, TensorType, Accessor, Function, dt >::SetUp()</a>, <a class="el" href="_normalization_layer_8h_source.xhtml#l00048">NormalizationLayer< DataSet, TensorType, Accessor, Function, dt >::SetUp()</a>, and <a class="el" href="_fully_connected_layer_8h_source.xhtml#l00048">FullyConnectedLayer< DataSet, TensorType, Accessor, Function, dt >::SetUp()</a>.</p>
+<p>Definition at line <a class="el" href="benchmark_2_c_l_2_depthwise_convolution_8cpp_source.xhtml#l00040">40</a> of file <a class="el" href="benchmark_2_c_l_2_depthwise_convolution_8cpp_source.xhtml">DepthwiseConvolution.cpp</a>.</p>
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- <td class="memname"><a class="el" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1042f1d7928dbde7edd7791e5b3a171b">validation::ValidationUserConfiguration</a> user_config</td>
+ <td class="memname">std::unique_ptr< <a class="el" href="classarm__compute_1_1test_1_1_assets_library.xhtml">AssetsLibrary</a> > library</td>
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-<p>Definition at line <a class="el" href="benchmark_2main_8cpp_source.xhtml#l00049">49</a> of file <a class="el" href="benchmark_2main_8cpp_source.xhtml">main.cpp</a>.</p>
+<p>Definition at line <a class="el" href="main_8cpp_source.xhtml#l00054">54</a> of file <a class="el" href="main_8cpp_source.xhtml">main.cpp</a>.</p>
-<p>Referenced by <a class="el" href="validation_2main_8cpp_source.xhtml#l00071">init_unit_test()</a>, and <a class="el" href="benchmark_2main_8cpp_source.xhtml#l00054">main()</a>.</p>
+<p>Referenced by <a class="el" href="_c_l_2_harris_corners_8cpp_source.xhtml#l00057">arm_compute::test::validation::DATA_TEST_CASE()</a>, <a class="el" href="_le_net5_network_8h_source.xhtml#l00152">LeNet5Network< TensorType, arm_compute::test::Accessor, ActivationLayerFunction, ConvolutionLayerFunction, FullyConnectedLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction >::feed()</a>, <a class="el" href="_alex_net_network_8h_source.xhtml#l00413">AlexNetNetwork< ITensorType, TensorType, SubTensorType, arm_compute::test::Accessor, ActivationLayerFunction, ConvolutionLayerFunction, DirectConvolutionLayerFunction, FullyConnectedLayerFunction, NormalizationLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction >::feed()</a>, <a class="el" href="_le_net5_network_8h_source.xhtml#l00136">LeNet5Network< TensorType, arm_compute::test::Accessor, ActivationLayerFunction, ConvolutionLayerFunction, FullyConnectedLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction >::fill()</a>, <a class="el" href="_alex_net_network_8h_source.xhtml#l00396">AlexNetNetwork< ITensorType, TensorType, SubTensorType, arm_compute::test::Accessor, ActivationLayerFunction, ConvolutionLayerFunction, DirectConvolutionLayerFunction, FullyConnectedLayerFunction, NormalizationLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction >::fill()</a>, <a class="el" href="tests_2validation_2_helpers_8h_source.xhtml#l00169">arm_compute::test::validation::fill_lookuptable()</a>, <a class="el" href="_helpers_8cpp_source.xhtml#l00032">arm_compute::test::validation::fill_mask_from_pattern()</a>, <a class="el" href="_le_net5_network_8h_source.xhtml#l00120">LeNet5Network< TensorType, arm_compute::test::Accessor, ActivationLayerFunction, ConvolutionLayerFunction, FullyConnectedLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction >::fill_random()</a>, <a class="el" href="_alex_net_network_8h_source.xhtml#l00346">AlexNetNetwork< ITensorType, TensorType, SubTensorType, arm_compute::test::Accessor, ActivationLayerFunction, ConvolutionLayerFunction, DirectConvolutionLayerFunction, FullyConnectedLayerFunction, NormalizationLayerFunction, PoolingLayerFunction, SoftmaxLayerFunction >::fill_random()</a>, <a class="el" href="_helper_8h_source.xhtml#l00039">fill_tensors()</a>, <a class="el" href="_helpers_8cpp_source.xhtml#l00098">arm_compute::test::validation::harris_corners_parameters()</a>, <a class="el" href="main_8cpp_source.xhtml#l00058">main()</a>, <a class="el" href="benchmark_2fixtures_2_activation_layer_fixture_8h_source.xhtml#l00043">ActivationLayerFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_fully_connected_layer_fixture_8h_source.xhtml#l00043">FullyConnectedLayerFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_convolution_layer_fixture_8h_source.xhtml#l00043">ConvolutionLayerFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_g_e_m_m_fixture_8h_source.xhtml#l00043">GEMMFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_floor_fixture_8h_source.xhtml#l00043">FloorFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_normalization_layer_fixture_8h_source.xhtml#l00043">NormalizationLayerFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_depthwise_separable_convolution_layer_fixture_8h_source.xhtml#l00043">DepthwiseSeparableConvolutionLayerFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_pooling_layer_fixture_8h_source.xhtml#l00043">PoolingLayerFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_depthwise_convolution_fixture_8h_source.xhtml#l00043">DepthwiseConvolutionFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="benchmark_2fixtures_2_batch_normalization_layer_fixture_8h_source.xhtml#l00043">BatchNormalizationLayerFixture< TensorType, Function, Accessor >::setup()</a>, <a class="el" href="_r_o_i_pooling_layer_fixture_8h_source.xhtml#l00045">ROIPoolingLayerFixture< TensorType, Function, Accessor, Array_T, ArrayAccessor >::setup()</a>, <a class="el" href="_scale_fixture_8h_source.xhtml#l00047">ScaleValidationFixture< TensorType, AccessorType, FunctionType, T >::setup()</a>, <a class="el" href="_non_linear_filter_fixture_8h_source.xhtml#l00048">NonLinearFilterValidationFixture< TensorType, AccessorType, FunctionType, T >::setup()</a>, <a class="el" href="_gaussian3x3_fixture_8h_source.xhtml#l00049">Gaussian3x3ValidationFixture< TensorType, AccessorType, FunctionType, T >::setup()</a>, <a class="el" href="_gaussian5x5_fixture_8h_source.xhtml#l00049">Gaussian5x5ValidationFixture< TensorType, AccessorType, FunctionType, T >::setup()</a>, <a class="el" href="_box3x3_fixture_8h_source.xhtml#l00049">Box3x3ValidationFixture< TensorType, AccessorType, FunctionType, T >::setup()</a>, <a class="el" href="_depth_concatenate_layer_fixture_8h_source.xhtml#l00050">DepthConcatenateValidationFixture< TensorType, ITensorType, AccessorType, FunctionType, T >::setup()</a>, and <a class="el" href="_sobel_fixture_8h_source.xhtml#l00105">SobelValidationFixture< TensorType, AccessorType, FunctionType, T, U >::setup()</a>.</p>
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@@ -1267,9 +7703,9 @@
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="namespacearm__compute.xhtml">arm_compute</a></li><li class="navelem"><a class="el" href="namespacearm__compute_1_1test.xhtml">test</a></li>
- <li class="footer">Generated on Fri Jun 23 2017 15:44:35 for Compute Library by
+ <li class="footer">Generated on Thu Sep 28 2017 14:37:58 for Compute Library by
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- <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
+ <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.6 </li>
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