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Ryan OSheade36e4a2020-03-13 16:26:19 +0000[diff] [blame]1<!-- Copyright (c) 2020 ARM Limited. -->
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100<div class="title">ConcatTestImpl.cpp File Reference</div> </div>
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102<div class="contents">
103<div class="textblock"><code>#include &quot;<a class="el" href="_concat_test_impl_8hpp_source.xhtml">ConcatTestImpl.hpp</a>&quot;</code><br />
104<code>#include &lt;<a class="el" href="_quantize_helper_8hpp_source.xhtml">QuantizeHelper.hpp</a>&gt;</code><br />
105<code>#include &lt;<a class="el" href="_resolve_type_8hpp_source.xhtml">ResolveType.hpp</a>&gt;</code><br />
106<code>#include &lt;<a class="el" href="_permute_8hpp_source.xhtml">armnnUtils/Permute.hpp</a>&gt;</code><br />
107<code>#include &lt;<a class="el" href="_tensor_copy_utils_8hpp_source.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>&gt;</code><br />
108<code>#include &lt;<a class="el" href="_workload_test_utils_8hpp_source.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>&gt;</code><br />
109<code>#include &lt;<a class="el" href="_tensor_helpers_8hpp_source.xhtml">test/TensorHelpers.hpp</a>&gt;</code><br />
110</div>
111<p><a href="_concat_test_impl_8cpp_source.xhtml">Go to the source code of this file.</a></p>
112<table class="memberdecls">
113<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
114Functions</h2></td></tr>
115<tr class="memitem:a908c80ff86d48fe1bc7cd4d4b1d00147"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a908c80ff86d48fe1bc7cd4d4b1d00147">CreateDescriptorForConcat</a> (const std::vector&lt; <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt; &amp;inputTensorInfos, unsigned int concatDim)</td></tr>
116<tr class="separator:a908c80ff86d48fe1bc7cd4d4b1d00147"><td class="memSeparator" colspan="2">&#160;</td></tr>
117<tr class="memitem:a905e011ae8536bbd643dd09495524596"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a905e011ae8536bbd643dd09495524596">NeedPermuteForConcat</a> (const std::vector&lt; <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt; &amp;inputTensorInfos, unsigned int concatDim)</td></tr>
118<tr class="separator:a905e011ae8536bbd643dd09495524596"><td class="memSeparator" colspan="2">&#160;</td></tr>
119<tr class="memitem:a8fcf10f2804bcbbfef4fd86ef6a5ff2d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a> (const <a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> &amp;inputShape)</td></tr>
120<tr class="separator:a8fcf10f2804bcbbfef4fd86ef6a5ff2d"><td class="memSeparator" colspan="2">&#160;</td></tr>
121<tr class="memitem:abd92409a35f1f4c41ee52c7471936fd8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#abd92409a35f1f4c41ee52c7471936fd8">Generate3dPermuteVectorForConcat</a> (unsigned int numDimensions, unsigned int &amp;concatDim, std::pair&lt; <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>, <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &gt; &amp;permutations)</td></tr>
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123<tr class="memitem:a64d353b468c3a9ec4b783a06cf59cb42"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
124<tr class="memitem:a64d353b468c3a9ec4b783a06cf59cb42"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a64d353b468c3a9ec4b783a06cf59cb42">PermuteTensorData</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &amp;mappings, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;inputTensorInfo, const T *inputData, std::vector&lt; T &gt; &amp;outputData)</td></tr>
125<tr class="separator:a64d353b468c3a9ec4b783a06cf59cb42"><td class="memSeparator" colspan="2">&#160;</td></tr>
126<tr class="memitem:a501616a77a3c7ca6d809c52e52da6ae3"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
127<tr class="memitem:a501616a77a3c7ca6d809c52e52da6ae3"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a501616a77a3c7ca6d809c52e52da6ae3">PermuteInputsForConcat</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, std::vector&lt; <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt; &amp;inputTensorInfos, std::vector&lt; T *&gt; &amp;inputData, std::vector&lt; std::vector&lt; T &gt;&gt; &amp;inputDataStorage, <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &amp;permuteVector, unsigned int &amp;concatDim, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;outputTensorInfo)</td></tr>
128<tr class="separator:a501616a77a3c7ca6d809c52e52da6ae3"><td class="memSeparator" colspan="2">&#160;</td></tr>
129<tr class="memitem:a46079932a4f92d02da9b0b538ddca52c"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
130<tr class="memitem:a46079932a4f92d02da9b0b538ddca52c"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a46079932a4f92d02da9b0b538ddca52c">PermuteOutputForConcat</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;tensorInfo, const <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &amp;permuteVector, std::unique_ptr&lt; <a class="el" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a> &gt; &amp;&amp;inputDataHandle, T *data)</td></tr>
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132<tr class="memitem:a3a7534d69e8cc11c52b0a056ca82bcb8"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
133<tr class="memitem:a3a7534d69e8cc11c52b0a056ca82bcb8"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a3a7534d69e8cc11c52b0a056ca82bcb8">Concatenate</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, std::initializer_list&lt; const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt; inputTensorInfosOrig, std::initializer_list&lt; T *&gt; inputsOrig, const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;outputTensorInfoOrig, T *output, unsigned int concatDim, bool useSubtensor)</td></tr>
134<tr class="separator:a3a7534d69e8cc11c52b0a056ca82bcb8"><td class="memSeparator" colspan="2">&#160;</td></tr>
135<tr class="memitem:a5bc6bee451406f7c6332ef1f6f88967c"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
136<tr class="memitem:a5bc6bee451406f7c6332ef1f6f88967c"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 1 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a5bc6bee451406f7c6332ef1f6f88967c">Concat1dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
137<tr class="separator:a5bc6bee451406f7c6332ef1f6f88967c"><td class="memSeparator" colspan="2">&#160;</td></tr>
138<tr class="memitem:a73214e9f0561ba98a6ba4824c7e69dbc"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
139<tr class="memitem:a73214e9f0561ba98a6ba4824c7e69dbc"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 2 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a73214e9f0561ba98a6ba4824c7e69dbc">Concat2dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;outputTensorInfo, unsigned int dimension, const float qScale, const int32_t qOffset)</td></tr>
140<tr class="separator:a73214e9f0561ba98a6ba4824c7e69dbc"><td class="memSeparator" colspan="2">&#160;</td></tr>
141<tr class="memitem:aed01fd1abcd334c4b36c8846f9c5cf83"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
142<tr class="memitem:aed01fd1abcd334c4b36c8846f9c5cf83"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 2 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#aed01fd1abcd334c4b36c8846f9c5cf83">Concat2dDim0TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
143<tr class="separator:aed01fd1abcd334c4b36c8846f9c5cf83"><td class="memSeparator" colspan="2">&#160;</td></tr>
144<tr class="memitem:a5f5b1d554f06515b564fb563c9b8c127"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
145<tr class="memitem:a5f5b1d554f06515b564fb563c9b8c127"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 2 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a5f5b1d554f06515b564fb563c9b8c127">Concat2dDim1TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
146<tr class="separator:a5f5b1d554f06515b564fb563c9b8c127"><td class="memSeparator" colspan="2">&#160;</td></tr>
147<tr class="memitem:a31b2beb6cd6e0fd9a68cb89b8b0378dc"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
148<tr class="memitem:a31b2beb6cd6e0fd9a68cb89b8b0378dc"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 2 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a31b2beb6cd6e0fd9a68cb89b8b0378dc">Concat2dDim0DiffInputDimsTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
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150<tr class="memitem:a921e963873d927a5acf4807572c0d374"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
151<tr class="memitem:a921e963873d927a5acf4807572c0d374"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 2 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a921e963873d927a5acf4807572c0d374">Concat2dDim1DiffInputDimsTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
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153<tr class="memitem:a7fbe775cdbc1967d651a97702a0eb08f"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
154<tr class="memitem:a7fbe775cdbc1967d651a97702a0eb08f"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a7fbe775cdbc1967d651a97702a0eb08f">Concat3dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;outputTensorInfo, unsigned int dimension, bool useSubtensor, float qScale, int32_t qOffset)</td></tr>
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156<tr class="memitem:ab129fe939f6a83daeecd9802c2024799"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
157<tr class="memitem:ab129fe939f6a83daeecd9802c2024799"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#ab129fe939f6a83daeecd9802c2024799">Concat3dDim0TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
158<tr class="separator:ab129fe939f6a83daeecd9802c2024799"><td class="memSeparator" colspan="2">&#160;</td></tr>
159<tr class="memitem:a79b36066d3bbd4ce6a61c081ea863ad7"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
160<tr class="memitem:a79b36066d3bbd4ce6a61c081ea863ad7"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a79b36066d3bbd4ce6a61c081ea863ad7">Concat3dDim1TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
161<tr class="separator:a79b36066d3bbd4ce6a61c081ea863ad7"><td class="memSeparator" colspan="2">&#160;</td></tr>
162<tr class="memitem:a89188ab52e61bc27b6e6bc4ccc81a413"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
163<tr class="memitem:a89188ab52e61bc27b6e6bc4ccc81a413"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a89188ab52e61bc27b6e6bc4ccc81a413">Concat3dDim2TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor, float qScale, int32_t qOffset)</td></tr>
164<tr class="separator:a89188ab52e61bc27b6e6bc4ccc81a413"><td class="memSeparator" colspan="2">&#160;</td></tr>
165<tr class="memitem:aed8a32c1d927c684bd76ce2e30a949fe"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
166<tr class="memitem:aed8a32c1d927c684bd76ce2e30a949fe"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#aed8a32c1d927c684bd76ce2e30a949fe">Concat3dDim0DiffInputDimsTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
167<tr class="separator:aed8a32c1d927c684bd76ce2e30a949fe"><td class="memSeparator" colspan="2">&#160;</td></tr>
168<tr class="memitem:a0c6ca29f4bf7c7fa4883fa73b5488b1a"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
169<tr class="memitem:a0c6ca29f4bf7c7fa4883fa73b5488b1a"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a0c6ca29f4bf7c7fa4883fa73b5488b1a">Concat3dDim1DiffInputDimsTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
170<tr class="separator:a0c6ca29f4bf7c7fa4883fa73b5488b1a"><td class="memSeparator" colspan="2">&#160;</td></tr>
171<tr class="memitem:a8af1d375ac13d009cf818825b343ec1c"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
172<tr class="memitem:a8af1d375ac13d009cf818825b343ec1c"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a8af1d375ac13d009cf818825b343ec1c">Concat3dDim2DiffInputDimsTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor, float qScale, int32_t qOffset)</td></tr>
173<tr class="separator:a8af1d375ac13d009cf818825b343ec1c"><td class="memSeparator" colspan="2">&#160;</td></tr>
174<tr class="memitem:aeef13eb0a86ade1b1c92357c44ed8add"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
175<tr class="memitem:aeef13eb0a86ade1b1c92357c44ed8add"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#aeef13eb0a86ade1b1c92357c44ed8add">Concat4dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;outputTensorInfo, unsigned int dimension, bool useSubtensor, float qScale, int32_t qOffset)</td></tr>
176<tr class="separator:aeef13eb0a86ade1b1c92357c44ed8add"><td class="memSeparator" colspan="2">&#160;</td></tr>
177<tr class="memitem:a59d4515193d877da62a352fc299d6d0f"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
178<tr class="memitem:a59d4515193d877da62a352fc299d6d0f"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a59d4515193d877da62a352fc299d6d0f">Concat4dDim0TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
179<tr class="separator:a59d4515193d877da62a352fc299d6d0f"><td class="memSeparator" colspan="2">&#160;</td></tr>
180<tr class="memitem:ac0a20ee6a32563959bbbbd16358d2a07"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
181<tr class="memitem:ac0a20ee6a32563959bbbbd16358d2a07"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#ac0a20ee6a32563959bbbbd16358d2a07">Concat4dDim1TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
182<tr class="separator:ac0a20ee6a32563959bbbbd16358d2a07"><td class="memSeparator" colspan="2">&#160;</td></tr>
183<tr class="memitem:ad14affe1f35650404637e949e6cda6d7"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
184<tr class="memitem:ad14affe1f35650404637e949e6cda6d7"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#ad14affe1f35650404637e949e6cda6d7">Concat4dDim2TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
185<tr class="separator:ad14affe1f35650404637e949e6cda6d7"><td class="memSeparator" colspan="2">&#160;</td></tr>
186<tr class="memitem:a5d8473a59cf76ad1914b36fd8d45f00b"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
187<tr class="memitem:a5d8473a59cf76ad1914b36fd8d45f00b"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a5d8473a59cf76ad1914b36fd8d45f00b">Concat4dDim3TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool useSubtensor)</td></tr>
188<tr class="separator:a5d8473a59cf76ad1914b36fd8d45f00b"><td class="memSeparator" colspan="2">&#160;</td></tr>
189<tr class="memitem:a00d88e24db4f4af21b6ba36d206a296c"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
190<tr class="memitem:a00d88e24db4f4af21b6ba36d206a296c"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a00d88e24db4f4af21b6ba36d206a296c">Concat4dDiffShapeDim0TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
191<tr class="separator:a00d88e24db4f4af21b6ba36d206a296c"><td class="memSeparator" colspan="2">&#160;</td></tr>
192<tr class="memitem:afca22d4151120b94ca2c68c662193cc1"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
193<tr class="memitem:afca22d4151120b94ca2c68c662193cc1"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#afca22d4151120b94ca2c68c662193cc1">Concat4dDiffShapeDim1TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
194<tr class="separator:afca22d4151120b94ca2c68c662193cc1"><td class="memSeparator" colspan="2">&#160;</td></tr>
195<tr class="memitem:a75ce8fbfdee084faa855d8e61d09b56d"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
196<tr class="memitem:a75ce8fbfdee084faa855d8e61d09b56d"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a75ce8fbfdee084faa855d8e61d09b56d">Concat4dDiffShapeDim2TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset)</td></tr>
197<tr class="separator:a75ce8fbfdee084faa855d8e61d09b56d"><td class="memSeparator" colspan="2">&#160;</td></tr>
198<tr class="memitem:a6318384f0f00e73bd26e43b7c4ca7735"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T = ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
199<tr class="memitem:a6318384f0f00e73bd26e43b7c4ca7735"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 4 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a6318384f0f00e73bd26e43b7c4ca7735">Concat4dDiffShapeDim3TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool useSubtensor)</td></tr>
200<tr class="separator:a6318384f0f00e73bd26e43b7c4ca7735"><td class="memSeparator" colspan="2">&#160;</td></tr>
201<tr class="memitem:a9d679b4a18c9cadc563bd77a726a3726"><td class="memTemplParams" colspan="2">template&lt;DataType ArmnnType, typename T &gt; </td></tr>
202<tr class="memitem:a9d679b4a18c9cadc563bd77a726a3726"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; T, 3 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a9d679b4a18c9cadc563bd77a726a3726">ConcatDifferentInputOutputQParamTest</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor)</td></tr>
203<tr class="separator:a9d679b4a18c9cadc563bd77a726a3726"><td class="memSeparator" colspan="2">&#160;</td></tr>
204<tr class="memitem:ac4a1dff653419576cd96b81cf10b984e"><td class="memItemLeft" align="right" valign="top">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; <a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType</a>&lt; DataType::QAsymmU8 &gt;, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#ac4a1dff653419576cd96b81cf10b984e">ConcatDifferentInputOutputQParamTest&lt; DataType::QAsymmU8 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor)</td></tr>
205<tr class="separator:ac4a1dff653419576cd96b81cf10b984e"><td class="memSeparator" colspan="2">&#160;</td></tr>
206<tr class="memitem:a878b6bd50169d509d8ee47d79e3c87d0"><td class="memItemLeft" align="right" valign="top">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; <a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType</a>&lt; DataType::QSymmS16 &gt;, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a878b6bd50169d509d8ee47d79e3c87d0">ConcatDifferentInputOutputQParamTest&lt; DataType::QSymmS16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor)</td></tr>
207<tr class="separator:a878b6bd50169d509d8ee47d79e3c87d0"><td class="memSeparator" colspan="2">&#160;</td></tr>
208<tr class="memitem:a4d293b286db068580f9d72048d4d7bfc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; float, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a4d293b286db068580f9d72048d4d7bfc">ConcatTest</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
209<tr class="separator:a4d293b286db068580f9d72048d4d7bfc"><td class="memSeparator" colspan="2">&#160;</td></tr>
210<tr class="memitem:ad4e20b0bf58dfbdbfaa93f445c5a7fbb"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; float, 1 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#ad4e20b0bf58dfbdbfaa93f445c5a7fbb">Concat1dTest</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
211<tr class="separator:ad4e20b0bf58dfbdbfaa93f445c5a7fbb"><td class="memSeparator" colspan="2">&#160;</td></tr>
212<tr class="memitem:a916c9acb126444caa775d14c635acaf8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; float, 2 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a916c9acb126444caa775d14c635acaf8">Concat2dDim0Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
213<tr class="separator:a916c9acb126444caa775d14c635acaf8"><td class="memSeparator" colspan="2">&#160;</td></tr>
214<tr class="memitem:aa786ba656ce7f53cc93692eec4645f6b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; float, 2 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#aa786ba656ce7f53cc93692eec4645f6b">Concat2dDim1Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
215<tr class="separator:aa786ba656ce7f53cc93692eec4645f6b"><td class="memSeparator" colspan="2">&#160;</td></tr>
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270<tr class="memitem:a8e409cdc677af52ce07c5cdc8ec63678"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a8e409cdc677af52ce07c5cdc8ec63678">Concat3dDim1Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
271<tr class="separator:a8e409cdc677af52ce07c5cdc8ec63678"><td class="memSeparator" colspan="2">&#160;</td></tr>
272<tr class="memitem:a75091ca6eb52deea2ce14ad8f6261236"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a75091ca6eb52deea2ce14ad8f6261236">Concat3dDim2Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor)</td></tr>
273<tr class="separator:a75091ca6eb52deea2ce14ad8f6261236"><td class="memSeparator" colspan="2">&#160;</td></tr>
274<tr class="memitem:afa4c2db58080ed0749c5e7c64f23af04"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#afa4c2db58080ed0749c5e7c64f23af04">Concat3dDim0DiffInputDimsUint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
275<tr class="separator:afa4c2db58080ed0749c5e7c64f23af04"><td class="memSeparator" colspan="2">&#160;</td></tr>
276<tr class="memitem:a1f8ad3cf8df29398ea04eaa4c790a100"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a1f8ad3cf8df29398ea04eaa4c790a100">Concat3dDim1DiffInputDimsUint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
277<tr class="separator:a1f8ad3cf8df29398ea04eaa4c790a100"><td class="memSeparator" colspan="2">&#160;</td></tr>
278<tr class="memitem:a6a0578f5cabc3b13c8800066d094f08b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 3 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a6a0578f5cabc3b13c8800066d094f08b">Concat3dDim2DiffInputDimsUint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor)</td></tr>
279<tr class="separator:a6a0578f5cabc3b13c8800066d094f08b"><td class="memSeparator" colspan="2">&#160;</td></tr>
280<tr class="memitem:a7b3adb97b81ab7b464c566caa3a231ba"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a7b3adb97b81ab7b464c566caa3a231ba">Concat4dDim0Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
281<tr class="separator:a7b3adb97b81ab7b464c566caa3a231ba"><td class="memSeparator" colspan="2">&#160;</td></tr>
282<tr class="memitem:aa13bf446c9b813c55ce96b49e5a17154"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#aa13bf446c9b813c55ce96b49e5a17154">Concat4dDim1Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
283<tr class="separator:aa13bf446c9b813c55ce96b49e5a17154"><td class="memSeparator" colspan="2">&#160;</td></tr>
284<tr class="memitem:a9a1400c7948e6536489676848c40630f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a9a1400c7948e6536489676848c40630f">Concat4dDim2Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
285<tr class="separator:a9a1400c7948e6536489676848c40630f"><td class="memSeparator" colspan="2">&#160;</td></tr>
286<tr class="memitem:a3de096f0e07787adaf34b6d348ca9543"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a3de096f0e07787adaf34b6d348ca9543">Concat4dDim3Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor)</td></tr>
287<tr class="separator:a3de096f0e07787adaf34b6d348ca9543"><td class="memSeparator" colspan="2">&#160;</td></tr>
288<tr class="memitem:a39a5321f36681cf1b7bbea885a0ccce9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a39a5321f36681cf1b7bbea885a0ccce9">Concat4dDiffShapeDim0Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
289<tr class="separator:a39a5321f36681cf1b7bbea885a0ccce9"><td class="memSeparator" colspan="2">&#160;</td></tr>
290<tr class="memitem:af5b51da08139262f68be752047e1b94c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#af5b51da08139262f68be752047e1b94c">Concat4dDiffShapeDim1Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
291<tr class="separator:af5b51da08139262f68be752047e1b94c"><td class="memSeparator" colspan="2">&#160;</td></tr>
292<tr class="memitem:a4ab1a7c2b554de49ef453e802eaf88a3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a4ab1a7c2b554de49ef453e802eaf88a3">Concat4dDiffShapeDim2Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager)</td></tr>
293<tr class="separator:a4ab1a7c2b554de49ef453e802eaf88a3"><td class="memSeparator" colspan="2">&#160;</td></tr>
294<tr class="memitem:a6852f3bb0b5a59260e0f76031e64cb3e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; uint8_t, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_concat_test_impl_8cpp.xhtml#a6852f3bb0b5a59260e0f76031e64cb3e">Concat4dDiffShapeDim3Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool useSubtensor)</td></tr>
295<tr class="separator:a6852f3bb0b5a59260e0f76031e64cb3e"><td class="memSeparator" colspan="2">&#160;</td></tr>
296</table>
297<h2 class="groupheader">Function Documentation</h2>
298<a id="ad4e20b0bf58dfbdbfaa93f445c5a7fbb"></a>
299<h2 class="memtitle"><span class="permalink"><a href="#ad4e20b0bf58dfbdbfaa93f445c5a7fbb">&#9670;&nbsp;</a></span>Concat1dTest()</h2>
300
301<div class="memitem">
302<div class="memproto">
303 <table class="memname">
304 <tr>
305 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 1&gt; Concat1dTest </td>
306 <td>(</td>
307 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
308 <td class="paramname"><em>workloadFactory</em>, </td>
309 </tr>
310 <tr>
311 <td class="paramkey"></td>
312 <td></td>
313 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
314 <td class="paramname"><em>memoryManager</em>&#160;</td>
315 </tr>
316 <tr>
317 <td></td>
318 <td>)</td>
319 <td></td><td></td>
320 </tr>
321 </table>
322</div><div class="memdoc">
323
324<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02197">2197</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
325<div class="fragment"><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>&#160;{</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>&#160; <span class="keywordflow">return</span> Concat1dTestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>&#160;}</div></div><!-- fragment -->
326</div>
327</div>
328<a id="a5bc6bee451406f7c6332ef1f6f88967c"></a>
329<h2 class="memtitle"><span class="permalink"><a href="#a5bc6bee451406f7c6332ef1f6f88967c">&#9670;&nbsp;</a></span>Concat1dTestImpl()</h2>
330
331<div class="memitem">
332<div class="memproto">
333 <table class="memname">
334 <tr>
335 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 1&gt; Concat1dTestImpl </td>
336 <td>(</td>
337 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
338 <td class="paramname"><em>workloadFactory</em>, </td>
339 </tr>
340 <tr>
341 <td class="paramkey"></td>
342 <td></td>
343 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
344 <td class="paramname"><em>memoryManager</em>, </td>
345 </tr>
346 <tr>
347 <td class="paramkey"></td>
348 <td></td>
349 <td class="paramtype">float&#160;</td>
350 <td class="paramname"><em>qScale</em>, </td>
351 </tr>
352 <tr>
353 <td class="paramkey"></td>
354 <td></td>
355 <td class="paramtype">int32_t&#160;</td>
356 <td class="paramname"><em>qOffset</em>&#160;</td>
357 </tr>
358 <tr>
359 <td></td>
360 <td>)</td>
361 <td></td><td></td>
362 </tr>
363 </table>
364</div><div class="memdoc">
365
366<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00413">413</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
367<div class="fragment"><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160;{</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 1&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;({ 1.0f, 2.0f, 3.0f }, qScale, qOffset));</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 1&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;({ 4.0f, 5.0f, 6.0f }, qScale, qOffset));</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 1&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;({ 7.0f, 8.0f, 9.0f }, qScale, qOffset));</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 9 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 1&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; { inputTensorInfo, inputTensorInfo, inputTensorInfo },</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; output.data(),</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; 0,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160;</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; result.output = MakeTensor&lt;T, 1&gt;(outputTensorInfo, output);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; result.outputExpected = MakeTensor&lt;T, 1&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; {</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; },</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
368<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
369</div><!-- fragment -->
370</div>
371</div>
372<a id="aed0a697e15183bbac585fde3535bdbd8"></a>
373<h2 class="memtitle"><span class="permalink"><a href="#aed0a697e15183bbac585fde3535bdbd8">&#9670;&nbsp;</a></span>Concat1dUint8Test()</h2>
374
375<div class="memitem">
376<div class="memproto">
377 <table class="memname">
378 <tr>
379 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 1&gt; Concat1dUint8Test </td>
380 <td>(</td>
381 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
382 <td class="paramname"><em>workloadFactory</em>, </td>
383 </tr>
384 <tr>
385 <td class="paramkey"></td>
386 <td></td>
387 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
388 <td class="paramname"><em>memoryManager</em>&#160;</td>
389 </tr>
390 <tr>
391 <td></td>
392 <td>)</td>
393 <td></td><td></td>
394 </tr>
395 </table>
396</div><div class="memdoc">
397
398<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02770">2770</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
399<div class="fragment"><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>&#160;{</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span>&#160; <span class="keywordflow">return</span> Concat1dTestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>&#160;}</div></div><!-- fragment -->
400</div>
401</div>
402<a id="ab5703ba71ea408eb6939a5be35b67a2f"></a>
403<h2 class="memtitle"><span class="permalink"><a href="#ab5703ba71ea408eb6939a5be35b67a2f">&#9670;&nbsp;</a></span>Concat2dDim0DiffInputDimsTest()</h2>
404
405<div class="memitem">
406<div class="memproto">
407 <table class="memname">
408 <tr>
409 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 2&gt; Concat2dDim0DiffInputDimsTest </td>
410 <td>(</td>
411 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
412 <td class="paramname"><em>workloadFactory</em>, </td>
413 </tr>
414 <tr>
415 <td class="paramkey"></td>
416 <td></td>
417 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
418 <td class="paramname"><em>memoryManager</em>&#160;</td>
419 </tr>
420 <tr>
421 <td></td>
422 <td>)</td>
423 <td></td><td></td>
424 </tr>
425 </table>
426</div><div class="memdoc">
427
428<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02218">2218</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
429<div class="fragment"><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>&#160;{</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>&#160; <span class="keywordflow">return</span> Concat2dDim0DiffInputDimsTestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>&#160;}</div></div><!-- fragment -->
430</div>
431</div>
432<a id="a31b2beb6cd6e0fd9a68cb89b8b0378dc"></a>
433<h2 class="memtitle"><span class="permalink"><a href="#a31b2beb6cd6e0fd9a68cb89b8b0378dc">&#9670;&nbsp;</a></span>Concat2dDim0DiffInputDimsTestImpl()</h2>
434
435<div class="memitem">
436<div class="memproto">
437 <table class="memname">
438 <tr>
439 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 2&gt; Concat2dDim0DiffInputDimsTestImpl </td>
440 <td>(</td>
441 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
442 <td class="paramname"><em>workloadFactory</em>, </td>
443 </tr>
444 <tr>
445 <td class="paramkey"></td>
446 <td></td>
447 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
448 <td class="paramname"><em>memoryManager</em>, </td>
449 </tr>
450 <tr>
451 <td class="paramkey"></td>
452 <td></td>
453 <td class="paramtype">float&#160;</td>
454 <td class="paramname"><em>qScale</em>, </td>
455 </tr>
456 <tr>
457 <td class="paramkey"></td>
458 <td></td>
459 <td class="paramtype">int32_t&#160;</td>
460 <td class="paramname"><em>qOffset</em>&#160;</td>
461 </tr>
462 <tr>
463 <td></td>
464 <td>)</td>
465 <td></td><td></td>
466 </tr>
467 </table>
468</div><div class="memdoc">
469
470<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00569">569</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
471<div class="fragment"><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>&#160;{</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 2&gt;(input0TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160; {</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160; 1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>&#160;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>&#160; 10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>&#160; },</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>&#160;</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 3, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 2&gt;(input1TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>&#160; {</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>&#160; 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>&#160;</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>&#160; 13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>&#160;</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>&#160; 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>&#160; },</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>&#160;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 1, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 2&gt;(input2TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>&#160; {</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>&#160; 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>&#160; },</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>&#160;</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>&#160;</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>&#160; { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>&#160; output.data(),</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>&#160; 0,</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>&#160;</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>&#160; result.output = MakeTensor&lt;T, 2&gt;(outputTensorInfo, output);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>&#160; result.outputExpected = MakeTensor&lt;T, 2&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>&#160; {</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>&#160; 1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>&#160;</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>&#160; 10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>&#160;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>&#160; <span class="comment">// Batch 2</span></div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>&#160; 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>&#160;</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>&#160; <span class="comment">// Batch 3</span></div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>&#160; 13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>&#160;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>&#160; <span class="comment">// Batch 4</span></div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>&#160; 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>&#160;</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>&#160; <span class="comment">// Batch 5</span></div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>&#160; 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>&#160; },</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>&#160;</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
472<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
473</div><!-- fragment -->
474</div>
475</div>
476<a id="a6990f89809b6699004e566a9d4f892f9"></a>
477<h2 class="memtitle"><span class="permalink"><a href="#a6990f89809b6699004e566a9d4f892f9">&#9670;&nbsp;</a></span>Concat2dDim0DiffInputDimsUint8Test()</h2>
478
479<div class="memitem">
480<div class="memproto">
481 <table class="memname">
482 <tr>
483 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 2&gt; Concat2dDim0DiffInputDimsUint8Test </td>
484 <td>(</td>
485 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
486 <td class="paramname"><em>workloadFactory</em>, </td>
487 </tr>
488 <tr>
489 <td class="paramkey"></td>
490 <td></td>
491 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
492 <td class="paramname"><em>memoryManager</em>&#160;</td>
493 </tr>
494 <tr>
495 <td></td>
496 <td>)</td>
497 <td></td><td></td>
498 </tr>
499 </table>
500</div><div class="memdoc">
501
502<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02791">2791</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
503<div class="fragment"><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span>&#160;{</div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>&#160; <span class="keywordflow">return</span> Concat2dDim0DiffInputDimsTestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>&#160; workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>&#160;}</div></div><!-- fragment -->
504</div>
505</div>
506<a id="a916c9acb126444caa775d14c635acaf8"></a>
507<h2 class="memtitle"><span class="permalink"><a href="#a916c9acb126444caa775d14c635acaf8">&#9670;&nbsp;</a></span>Concat2dDim0Test()</h2>
508
509<div class="memitem">
510<div class="memproto">
511 <table class="memname">
512 <tr>
513 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 2&gt; Concat2dDim0Test </td>
514 <td>(</td>
515 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
516 <td class="paramname"><em>workloadFactory</em>, </td>
517 </tr>
518 <tr>
519 <td class="paramkey"></td>
520 <td></td>
521 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
522 <td class="paramname"><em>memoryManager</em>&#160;</td>
523 </tr>
524 <tr>
525 <td></td>
526 <td>)</td>
527 <td></td><td></td>
528 </tr>
529 </table>
530</div><div class="memdoc">
531
532<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02204">2204</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
533<div class="fragment"><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>&#160;{</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>&#160; <span class="keywordflow">return</span> Concat2dDim0TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>&#160;}</div></div><!-- fragment -->
534</div>
535</div>
536<a id="aed01fd1abcd334c4b36c8846f9c5cf83"></a>
537<h2 class="memtitle"><span class="permalink"><a href="#aed01fd1abcd334c4b36c8846f9c5cf83">&#9670;&nbsp;</a></span>Concat2dDim0TestImpl()</h2>
538
539<div class="memitem">
540<div class="memproto">
541 <table class="memname">
542 <tr>
543 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 2&gt; Concat2dDim0TestImpl </td>
544 <td>(</td>
545 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
546 <td class="paramname"><em>workloadFactory</em>, </td>
547 </tr>
548 <tr>
549 <td class="paramkey"></td>
550 <td></td>
551 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
552 <td class="paramname"><em>memoryManager</em>, </td>
553 </tr>
554 <tr>
555 <td class="paramkey"></td>
556 <td></td>
557 <td class="paramtype">float&#160;</td>
558 <td class="paramname"><em>qScale</em>, </td>
559 </tr>
560 <tr>
561 <td class="paramkey"></td>
562 <td></td>
563 <td class="paramtype">int32_t&#160;</td>
564 <td class="paramname"><em>qOffset</em>&#160;</td>
565 </tr>
566 <tr>
567 <td></td>
568 <td>)</td>
569 <td></td><td></td>
570 </tr>
571 </table>
572</div><div class="memdoc">
573
574<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00507">507</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
575
576<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
577<div class="fragment"><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160;{</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> result = Concat2dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 0, qScale, qOffset);</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160;</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 2&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; {</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; 1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160;</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; 10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; <span class="comment">// Batch 2</span></div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160;</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; <span class="comment">// Batch 3</span></div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; 13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160;</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; <span class="comment">// Batch 4</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160;</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; <span class="comment">// Batch 5</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; },</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160;</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
578<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
579<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
580</div><!-- fragment -->
581</div>
582</div>
583<a id="a549f1d04a9747d0c3046e0b708d67116"></a>
584<h2 class="memtitle"><span class="permalink"><a href="#a549f1d04a9747d0c3046e0b708d67116">&#9670;&nbsp;</a></span>Concat2dDim0Uint8Test()</h2>
585
586<div class="memitem">
587<div class="memproto">
588 <table class="memname">
589 <tr>
590 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 2&gt; Concat2dDim0Uint8Test </td>
591 <td>(</td>
592 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
593 <td class="paramname"><em>workloadFactory</em>, </td>
594 </tr>
595 <tr>
596 <td class="paramkey"></td>
597 <td></td>
598 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
599 <td class="paramname"><em>memoryManager</em>&#160;</td>
600 </tr>
601 <tr>
602 <td></td>
603 <td>)</td>
604 <td></td><td></td>
605 </tr>
606 </table>
607</div><div class="memdoc">
608
609<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02777">2777</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
610<div class="fragment"><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span>&#160;{</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>&#160; <span class="keywordflow">return</span> Concat2dDim0TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>&#160;}</div></div><!-- fragment -->
611</div>
612</div>
613<a id="a142df3b6c7d699e7623fb37ff95e8c5a"></a>
614<h2 class="memtitle"><span class="permalink"><a href="#a142df3b6c7d699e7623fb37ff95e8c5a">&#9670;&nbsp;</a></span>Concat2dDim1DiffInputDimsTest()</h2>
615
616<div class="memitem">
617<div class="memproto">
618 <table class="memname">
619 <tr>
620 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 2&gt; Concat2dDim1DiffInputDimsTest </td>
621 <td>(</td>
622 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
623 <td class="paramname"><em>workloadFactory</em>, </td>
624 </tr>
625 <tr>
626 <td class="paramkey"></td>
627 <td></td>
628 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
629 <td class="paramname"><em>memoryManager</em>&#160;</td>
630 </tr>
631 <tr>
632 <td></td>
633 <td>)</td>
634 <td></td><td></td>
635 </tr>
636 </table>
637</div><div class="memdoc">
638
639<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02225">2225</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
640<div class="fragment"><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>&#160;{</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>&#160; <span class="keywordflow">return</span> Concat2dDim1DiffInputDimsTestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>&#160;}</div></div><!-- fragment -->
641</div>
642</div>
643<a id="a921e963873d927a5acf4807572c0d374"></a>
644<h2 class="memtitle"><span class="permalink"><a href="#a921e963873d927a5acf4807572c0d374">&#9670;&nbsp;</a></span>Concat2dDim1DiffInputDimsTestImpl()</h2>
645
646<div class="memitem">
647<div class="memproto">
648 <table class="memname">
649 <tr>
650 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 2&gt; Concat2dDim1DiffInputDimsTestImpl </td>
651 <td>(</td>
652 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
653 <td class="paramname"><em>workloadFactory</em>, </td>
654 </tr>
655 <tr>
656 <td class="paramkey"></td>
657 <td></td>
658 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
659 <td class="paramname"><em>memoryManager</em>, </td>
660 </tr>
661 <tr>
662 <td class="paramkey"></td>
663 <td></td>
664 <td class="paramtype">float&#160;</td>
665 <td class="paramname"><em>qScale</em>, </td>
666 </tr>
667 <tr>
668 <td class="paramkey"></td>
669 <td></td>
670 <td class="paramtype">int32_t&#160;</td>
671 <td class="paramname"><em>qOffset</em>&#160;</td>
672 </tr>
673 <tr>
674 <td></td>
675 <td>)</td>
676 <td></td><td></td>
677 </tr>
678 </table>
679</div><div class="memdoc">
680
681<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00648">648</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
682<div class="fragment"><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>&#160;{</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 2&gt;(input0TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>&#160; {</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>&#160; 1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>&#160;</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>&#160; 10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>&#160; },</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>&#160;</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 2, 5 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 2&gt;(input1TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>&#160; {</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>&#160; 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>&#160;</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>&#160; 13.0f, 14.0f, 15.0f, 16.0f, 17.0f,</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>&#160; },</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>&#160;</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 2, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 2&gt;(input2TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>&#160; {</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>&#160; 9.0f,</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>&#160;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>&#160; 18.0f</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>&#160; },</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>&#160;</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>&#160;</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>&#160; { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>&#160; output.data(),</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>&#160; 1,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>&#160;</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>&#160; result.output = MakeTensor&lt;T, 2&gt;(outputTensorInfo, output);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>&#160; result.outputExpected = MakeTensor&lt;T, 2&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>&#160; {</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>&#160; 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>&#160;</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>&#160; 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>&#160; },</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>&#160;</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
683<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
684</div><!-- fragment -->
685</div>
686</div>
687<a id="a398f4322d3f71cc0fe4a04831a556c91"></a>
688<h2 class="memtitle"><span class="permalink"><a href="#a398f4322d3f71cc0fe4a04831a556c91">&#9670;&nbsp;</a></span>Concat2dDim1DiffInputDimsUint8Test()</h2>
689
690<div class="memitem">
691<div class="memproto">
692 <table class="memname">
693 <tr>
694 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 2&gt; Concat2dDim1DiffInputDimsUint8Test </td>
695 <td>(</td>
696 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
697 <td class="paramname"><em>workloadFactory</em>, </td>
698 </tr>
699 <tr>
700 <td class="paramkey"></td>
701 <td></td>
702 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
703 <td class="paramname"><em>memoryManager</em>&#160;</td>
704 </tr>
705 <tr>
706 <td></td>
707 <td>)</td>
708 <td></td><td></td>
709 </tr>
710 </table>
711</div><div class="memdoc">
712
713<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02799">2799</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
714<div class="fragment"><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>&#160;{</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>&#160; <span class="keywordflow">return</span> Concat2dDim1DiffInputDimsTestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>&#160; workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span>&#160;}</div></div><!-- fragment -->
715</div>
716</div>
717<a id="aa786ba656ce7f53cc93692eec4645f6b"></a>
718<h2 class="memtitle"><span class="permalink"><a href="#aa786ba656ce7f53cc93692eec4645f6b">&#9670;&nbsp;</a></span>Concat2dDim1Test()</h2>
719
720<div class="memitem">
721<div class="memproto">
722 <table class="memname">
723 <tr>
724 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 2&gt; Concat2dDim1Test </td>
725 <td>(</td>
726 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
727 <td class="paramname"><em>workloadFactory</em>, </td>
728 </tr>
729 <tr>
730 <td class="paramkey"></td>
731 <td></td>
732 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
733 <td class="paramname"><em>memoryManager</em>&#160;</td>
734 </tr>
735 <tr>
736 <td></td>
737 <td>)</td>
738 <td></td><td></td>
739 </tr>
740 </table>
741</div><div class="memdoc">
742
743<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02211">2211</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
744<div class="fragment"><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>&#160;{</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>&#160; <span class="keywordflow">return</span> Concat2dDim1TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>&#160;}</div></div><!-- fragment -->
745</div>
746</div>
747<a id="a5f5b1d554f06515b564fb563c9b8c127"></a>
748<h2 class="memtitle"><span class="permalink"><a href="#a5f5b1d554f06515b564fb563c9b8c127">&#9670;&nbsp;</a></span>Concat2dDim1TestImpl()</h2>
749
750<div class="memitem">
751<div class="memproto">
752 <table class="memname">
753 <tr>
754 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 2&gt; Concat2dDim1TestImpl </td>
755 <td>(</td>
756 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
757 <td class="paramname"><em>workloadFactory</em>, </td>
758 </tr>
759 <tr>
760 <td class="paramkey"></td>
761 <td></td>
762 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
763 <td class="paramname"><em>memoryManager</em>, </td>
764 </tr>
765 <tr>
766 <td class="paramkey"></td>
767 <td></td>
768 <td class="paramtype">float&#160;</td>
769 <td class="paramname"><em>qScale</em>, </td>
770 </tr>
771 <tr>
772 <td class="paramkey"></td>
773 <td></td>
774 <td class="paramtype">int32_t&#160;</td>
775 <td class="paramname"><em>qOffset</em>&#160;</td>
776 </tr>
777 <tr>
778 <td></td>
779 <td>)</td>
780 <td></td><td></td>
781 </tr>
782 </table>
783</div><div class="memdoc">
784
785<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00544">544</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
786
787<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
788<div class="fragment"><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160;{</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> result = Concat2dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 1, qScale, qOffset);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160;</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 2&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; {</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160; 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160; 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>&#160; },</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
789<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
790<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
791</div><!-- fragment -->
792</div>
793</div>
794<a id="a71bbfb11850812db44a607d2f9c39681"></a>
795<h2 class="memtitle"><span class="permalink"><a href="#a71bbfb11850812db44a607d2f9c39681">&#9670;&nbsp;</a></span>Concat2dDim1Uint8Test()</h2>
796
797<div class="memitem">
798<div class="memproto">
799 <table class="memname">
800 <tr>
801 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 2&gt; Concat2dDim1Uint8Test </td>
802 <td>(</td>
803 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
804 <td class="paramname"><em>workloadFactory</em>, </td>
805 </tr>
806 <tr>
807 <td class="paramkey"></td>
808 <td></td>
809 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
810 <td class="paramname"><em>memoryManager</em>&#160;</td>
811 </tr>
812 <tr>
813 <td></td>
814 <td>)</td>
815 <td></td><td></td>
816 </tr>
817 </table>
818</div><div class="memdoc">
819
820<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02784">2784</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
821<div class="fragment"><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span>&#160;{</div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>&#160; <span class="keywordflow">return</span> Concat2dDim1TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>&#160;}</div></div><!-- fragment -->
822</div>
823</div>
824<a id="a73214e9f0561ba98a6ba4824c7e69dbc"></a>
825<h2 class="memtitle"><span class="permalink"><a href="#a73214e9f0561ba98a6ba4824c7e69dbc">&#9670;&nbsp;</a></span>Concat2dTestImpl()</h2>
826
827<div class="memitem">
828<div class="memproto">
829 <table class="memname">
830 <tr>
831 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 2&gt; Concat2dTestImpl </td>
832 <td>(</td>
833 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
834 <td class="paramname"><em>workloadFactory</em>, </td>
835 </tr>
836 <tr>
837 <td class="paramkey"></td>
838 <td></td>
839 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
840 <td class="paramname"><em>memoryManager</em>, </td>
841 </tr>
842 <tr>
843 <td class="paramkey"></td>
844 <td></td>
845 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;&#160;</td>
846 <td class="paramname"><em>outputTensorInfo</em>, </td>
847 </tr>
848 <tr>
849 <td class="paramkey"></td>
850 <td></td>
851 <td class="paramtype">unsigned int&#160;</td>
852 <td class="paramname"><em>dimension</em>, </td>
853 </tr>
854 <tr>
855 <td class="paramkey"></td>
856 <td></td>
857 <td class="paramtype">const float&#160;</td>
858 <td class="paramname"><em>qScale</em>, </td>
859 </tr>
860 <tr>
861 <td class="paramkey"></td>
862 <td></td>
863 <td class="paramtype">const int32_t&#160;</td>
864 <td class="paramname"><em>qOffset</em>&#160;</td>
865 </tr>
866 <tr>
867 <td></td>
868 <td>)</td>
869 <td></td><td></td>
870 </tr>
871 </table>
872</div><div class="memdoc">
873
874<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00450">450</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
875
876<p class="reference">References <a class="el" href="_tensor_8hpp_source.xhtml#l00093">TensorInfo::GetNumElements()</a>, and <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>.</p>
877<div class="fragment"><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160;{</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 2&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; 1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; 10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; },</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160;</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 2&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; {</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; 13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; },</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 2&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; {</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; <span class="comment">// Batch 0</span></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; <span class="comment">// Batch 1</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; },</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160;</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; output.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; { inputTensorInfo, inputTensorInfo, inputTensorInfo },</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; output.data(),</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; dimension,</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; result.output = MakeTensor&lt;T, 2&gt;(outputTensorInfo, output);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
878<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
879<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00093">Tensor.hpp:93</a></div></div>
880</div><!-- fragment -->
881</div>
882</div>
883<a id="ad970167c99234cfcc22107efbe3503d3"></a>
884<h2 class="memtitle"><span class="permalink"><a href="#ad970167c99234cfcc22107efbe3503d3">&#9670;&nbsp;</a></span>Concat3dDim0DiffInputDimsTest()</h2>
885
886<div class="memitem">
887<div class="memproto">
888 <table class="memname">
889 <tr>
890 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 3&gt; Concat3dDim0DiffInputDimsTest </td>
891 <td>(</td>
892 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
893 <td class="paramname"><em>workloadFactory</em>, </td>
894 </tr>
895 <tr>
896 <td class="paramkey"></td>
897 <td></td>
898 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
899 <td class="paramname"><em>memoryManager</em>&#160;</td>
900 </tr>
901 <tr>
902 <td></td>
903 <td>)</td>
904 <td></td><td></td>
905 </tr>
906 </table>
907</div><div class="memdoc">
908
909<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02254">2254</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
910<div class="fragment"><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>&#160;{</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>&#160; <span class="keywordflow">return</span> Concat3dDim0DiffInputDimsTestImpl&lt;DataType::Float32&gt;(</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>&#160; workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>&#160;}</div></div><!-- fragment -->
911</div>
912</div>
913<a id="aed8a32c1d927c684bd76ce2e30a949fe"></a>
914<h2 class="memtitle"><span class="permalink"><a href="#aed8a32c1d927c684bd76ce2e30a949fe">&#9670;&nbsp;</a></span>Concat3dDim0DiffInputDimsTestImpl()</h2>
915
916<div class="memitem">
917<div class="memproto">
918 <table class="memname">
919 <tr>
920 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; Concat3dDim0DiffInputDimsTestImpl </td>
921 <td>(</td>
922 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
923 <td class="paramname"><em>workloadFactory</em>, </td>
924 </tr>
925 <tr>
926 <td class="paramkey"></td>
927 <td></td>
928 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
929 <td class="paramname"><em>memoryManager</em>, </td>
930 </tr>
931 <tr>
932 <td class="paramkey"></td>
933 <td></td>
934 <td class="paramtype">float&#160;</td>
935 <td class="paramname"><em>qScale</em>, </td>
936 </tr>
937 <tr>
938 <td class="paramkey"></td>
939 <td></td>
940 <td class="paramtype">int32_t&#160;</td>
941 <td class="paramname"><em>qOffset</em>&#160;</td>
942 </tr>
943 <tr>
944 <td></td>
945 <td>)</td>
946 <td></td><td></td>
947 </tr>
948 </table>
949</div><div class="memdoc">
950
951<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00993">993</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
952<div class="fragment"><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>&#160;{</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 3&gt;(input0TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>&#160; {</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>&#160;</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>&#160;</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>&#160;</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>&#160;</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160;</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>&#160; 23.0f, 24.0f</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>&#160; },</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>&#160;</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 1, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 3&gt;(input1TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>&#160; {</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>&#160;</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>&#160;</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>&#160; },</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>&#160;</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 3, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 3&gt;(input2TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>&#160; {</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>&#160;</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>&#160;</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>&#160;</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>&#160;</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>&#160;</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>&#160;</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>&#160; <span class="comment">// Batch 2, Channel 0</span></div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>&#160; 31.0f, 32.0f,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>&#160;</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>&#160; <span class="comment">// Batch 2, Channel 1</span></div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>&#160; 33.0f, 34.0f,</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>&#160;</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>&#160; <span class="comment">// Batch 2, Channel 2</span></div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>&#160; 35.0f, 36.0f</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>&#160; },</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>&#160;</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>&#160;</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>&#160; { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>&#160; output.data(),</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>&#160; 0,</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>&#160;</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>&#160; result.output = MakeTensor&lt;T, 3&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>&#160; result.outputExpected = MakeTensor&lt;T, 3&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>&#160; {</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>&#160;</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>&#160;</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>&#160;</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>&#160;</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>&#160;</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>&#160;</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>&#160; <span class="comment">// Batch 2, Channel 0</span></div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>&#160;</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>&#160; <span class="comment">// Batch 2, Channel 1</span></div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>&#160;</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>&#160; <span class="comment">// Batch 2, Channel 2</span></div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>&#160;</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>&#160; <span class="comment">// Batch 3, Channel 0</span></div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>&#160;</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>&#160; <span class="comment">// Batch 3, Channel 1</span></div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>&#160;</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>&#160; <span class="comment">// Batch 3, Channel 2</span></div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>&#160;</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>&#160; <span class="comment">// Batch 4, Channel 0</span></div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>&#160;</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>&#160; <span class="comment">// Batch 4, Channel 1</span></div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>&#160;</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>&#160; <span class="comment">// Batch 4, Channel 2</span></div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>&#160;</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>&#160; <span class="comment">// Batch 5, Channel 0</span></div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>&#160; 31.0f, 32.0f,</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>&#160;</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>&#160; <span class="comment">// Batch 5, Channel 1</span></div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>&#160; 33.0f, 34.0f,</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>&#160;</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>&#160; <span class="comment">// Batch 5, Channel 2</span></div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>&#160; 35.0f, 36.0f</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>&#160; },</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>&#160;</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
953<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
954</div><!-- fragment -->
955</div>
956</div>
957<a id="afa4c2db58080ed0749c5e7c64f23af04"></a>
958<h2 class="memtitle"><span class="permalink"><a href="#afa4c2db58080ed0749c5e7c64f23af04">&#9670;&nbsp;</a></span>Concat3dDim0DiffInputDimsUint8Test()</h2>
959
960<div class="memitem">
961<div class="memproto">
962 <table class="memname">
963 <tr>
964 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; Concat3dDim0DiffInputDimsUint8Test </td>
965 <td>(</td>
966 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
967 <td class="paramname"><em>workloadFactory</em>, </td>
968 </tr>
969 <tr>
970 <td class="paramkey"></td>
971 <td></td>
972 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
973 <td class="paramname"><em>memoryManager</em>&#160;</td>
974 </tr>
975 <tr>
976 <td></td>
977 <td>)</td>
978 <td></td><td></td>
979 </tr>
980 </table>
981</div><div class="memdoc">
982
983<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02830">2830</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
984<div class="fragment"><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span>&#160;{</div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>&#160; <span class="keywordflow">return</span> Concat3dDim0TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span>&#160;}</div></div><!-- fragment -->
985</div>
986</div>
987<a id="ad9391e74e0fcf3a9f2c08d6a865d910a"></a>
988<h2 class="memtitle"><span class="permalink"><a href="#ad9391e74e0fcf3a9f2c08d6a865d910a">&#9670;&nbsp;</a></span>Concat3dDim0Test()</h2>
989
990<div class="memitem">
991<div class="memproto">
992 <table class="memname">
993 <tr>
994 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 3&gt; Concat3dDim0Test </td>
995 <td>(</td>
996 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
997 <td class="paramname"><em>workloadFactory</em>, </td>
998 </tr>
999 <tr>
1000 <td class="paramkey"></td>
1001 <td></td>
1002 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1003 <td class="paramname"><em>memoryManager</em>&#160;</td>
1004 </tr>
1005 <tr>
1006 <td></td>
1007 <td>)</td>
1008 <td></td><td></td>
1009 </tr>
1010 </table>
1011</div><div class="memdoc">
1012
1013<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02232">2232</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1014<div class="fragment"><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>&#160;{</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>&#160; <span class="keywordflow">return</span> Concat3dDim0TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>&#160;}</div></div><!-- fragment -->
1015</div>
1016</div>
1017<a id="ab129fe939f6a83daeecd9802c2024799"></a>
1018<h2 class="memtitle"><span class="permalink"><a href="#ab129fe939f6a83daeecd9802c2024799">&#9670;&nbsp;</a></span>Concat3dDim0TestImpl()</h2>
1019
1020<div class="memitem">
1021<div class="memproto">
1022 <table class="memname">
1023 <tr>
1024 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; Concat3dDim0TestImpl </td>
1025 <td>(</td>
1026 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1027 <td class="paramname"><em>workloadFactory</em>, </td>
1028 </tr>
1029 <tr>
1030 <td class="paramkey"></td>
1031 <td></td>
1032 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1033 <td class="paramname"><em>memoryManager</em>, </td>
1034 </tr>
1035 <tr>
1036 <td class="paramkey"></td>
1037 <td></td>
1038 <td class="paramtype">float&#160;</td>
1039 <td class="paramname"><em>qScale</em>, </td>
1040 </tr>
1041 <tr>
1042 <td class="paramkey"></td>
1043 <td></td>
1044 <td class="paramtype">int32_t&#160;</td>
1045 <td class="paramname"><em>qOffset</em>&#160;</td>
1046 </tr>
1047 <tr>
1048 <td></td>
1049 <td>)</td>
1050 <td></td><td></td>
1051 </tr>
1052 </table>
1053</div><div class="memdoc">
1054
1055<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00809">809</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1056
1057<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
1058<div class="fragment"><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>&#160;{</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>&#160;</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> result = Concat3dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 0, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>&#160;</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 3&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>&#160; {</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>&#160;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>&#160;</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>&#160;</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>&#160;</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>&#160;</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>&#160;</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>&#160; <span class="comment">// Batch 2, Channel 0</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>&#160;</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>&#160; <span class="comment">// Batch 2, Channel 1</span></div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>&#160;</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>&#160; <span class="comment">// Batch 2, Channel 2</span></div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>&#160;</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>&#160; <span class="comment">// Batch 3, Channel 0</span></div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>&#160;</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>&#160; <span class="comment">// Batch 3, Channel 1</span></div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>&#160;</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>&#160; <span class="comment">// Batch 3, Channel 2</span></div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>&#160;</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>&#160; <span class="comment">// Batch 4, Channel 0</span></div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>&#160;</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>&#160; <span class="comment">// Batch 4, Channel 1</span></div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>&#160;</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>&#160; <span class="comment">// Batch 4, Channel 2</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>&#160;</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>&#160; <span class="comment">// Batch 5, Channel 0</span></div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>&#160; 31.0f, 32.0f,</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>&#160;</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>&#160; <span class="comment">// Batch 5, Channel 1</span></div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>&#160; 33.0f, 34.0f,</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>&#160;</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>&#160; <span class="comment">// Batch 5, Channel 2</span></div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>&#160; 35.0f, 36.0f</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>&#160; },</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>&#160;</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1059<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
1060<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1061</div><!-- fragment -->
1062</div>
1063</div>
1064<a id="a05e4c6d3c63851bebb99391e4af3ab6b"></a>
1065<h2 class="memtitle"><span class="permalink"><a href="#a05e4c6d3c63851bebb99391e4af3ab6b">&#9670;&nbsp;</a></span>Concat3dDim0Uint8Test()</h2>
1066
1067<div class="memitem">
1068<div class="memproto">
1069 <table class="memname">
1070 <tr>
1071 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; Concat3dDim0Uint8Test </td>
1072 <td>(</td>
1073 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1074 <td class="paramname"><em>workloadFactory</em>, </td>
1075 </tr>
1076 <tr>
1077 <td class="paramkey"></td>
1078 <td></td>
1079 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1080 <td class="paramname"><em>memoryManager</em>&#160;</td>
1081 </tr>
1082 <tr>
1083 <td></td>
1084 <td>)</td>
1085 <td></td><td></td>
1086 </tr>
1087 </table>
1088</div><div class="memdoc">
1089
1090<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02807">2807</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1091<div class="fragment"><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>&#160;{</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>&#160; <span class="keywordflow">return</span> Concat3dDim0TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>&#160;}</div></div><!-- fragment -->
1092</div>
1093</div>
1094<a id="a693e34e3f519f0323cb165468560ee72"></a>
1095<h2 class="memtitle"><span class="permalink"><a href="#a693e34e3f519f0323cb165468560ee72">&#9670;&nbsp;</a></span>Concat3dDim1DiffInputDimsTest()</h2>
1096
1097<div class="memitem">
1098<div class="memproto">
1099 <table class="memname">
1100 <tr>
1101 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 3&gt; Concat3dDim1DiffInputDimsTest </td>
1102 <td>(</td>
1103 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1104 <td class="paramname"><em>workloadFactory</em>, </td>
1105 </tr>
1106 <tr>
1107 <td class="paramkey"></td>
1108 <td></td>
1109 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1110 <td class="paramname"><em>memoryManager</em>&#160;</td>
1111 </tr>
1112 <tr>
1113 <td></td>
1114 <td>)</td>
1115 <td></td><td></td>
1116 </tr>
1117 </table>
1118</div><div class="memdoc">
1119
1120<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02262">2262</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1121<div class="fragment"><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>&#160;{</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>&#160; <span class="keywordflow">return</span> Concat3dDim1DiffInputDimsTestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>&#160;}</div></div><!-- fragment -->
1122</div>
1123</div>
1124<a id="a0c6ca29f4bf7c7fa4883fa73b5488b1a"></a>
1125<h2 class="memtitle"><span class="permalink"><a href="#a0c6ca29f4bf7c7fa4883fa73b5488b1a">&#9670;&nbsp;</a></span>Concat3dDim1DiffInputDimsTestImpl()</h2>
1126
1127<div class="memitem">
1128<div class="memproto">
1129 <table class="memname">
1130 <tr>
1131 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; Concat3dDim1DiffInputDimsTestImpl </td>
1132 <td>(</td>
1133 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1134 <td class="paramname"><em>workloadFactory</em>, </td>
1135 </tr>
1136 <tr>
1137 <td class="paramkey"></td>
1138 <td></td>
1139 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1140 <td class="paramname"><em>memoryManager</em>, </td>
1141 </tr>
1142 <tr>
1143 <td class="paramkey"></td>
1144 <td></td>
1145 <td class="paramtype">float&#160;</td>
1146 <td class="paramname"><em>qScale</em>, </td>
1147 </tr>
1148 <tr>
1149 <td class="paramkey"></td>
1150 <td></td>
1151 <td class="paramtype">int32_t&#160;</td>
1152 <td class="paramname"><em>qOffset</em>&#160;</td>
1153 </tr>
1154 <tr>
1155 <td></td>
1156 <td>)</td>
1157 <td></td><td></td>
1158 </tr>
1159 </table>
1160</div><div class="memdoc">
1161
1162<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01144">1144</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1163<div class="fragment"><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>&#160;{</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 3&gt;(input0TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>&#160; {</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>&#160;</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>&#160;</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>&#160;</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>&#160;</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>&#160;</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>&#160; 23.0f, 24.0f</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>&#160; },</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>&#160;</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 2, 4, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 3&gt;(input1TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>&#160; {</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>&#160;</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>&#160;</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>&#160;</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>&#160; <span class="comment">// Batch 0, Channel 3</span></div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>&#160;</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>&#160;</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>&#160;</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>&#160;</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>&#160; <span class="comment">// Batch 1, Channel 3</span></div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>&#160; },</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>&#160;</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 2, 1, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 3&gt;(input2TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>&#160; {</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>&#160;</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>&#160; 31.0f, 32.0f,</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>&#160; },</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>&#160;</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 8, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>&#160;</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>&#160; { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>&#160; output.data(),</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>&#160; 1,</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>&#160;</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>&#160; result.output = MakeTensor&lt;T, 3&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>&#160; result.outputExpected = MakeTensor&lt;T, 3&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>&#160; {</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>&#160;</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>&#160;</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>&#160;</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>&#160; <span class="comment">// Batch 0, Channel 3</span></div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>&#160;</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>&#160; <span class="comment">// Batch 0, Channel 4</span></div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>&#160;</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>&#160; <span class="comment">// Batch 0, Channel 5</span></div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>&#160;</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>&#160; <span class="comment">// Batch 0, Channel 6</span></div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>&#160;</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>&#160; <span class="comment">// Batch 0, Channel 7</span></div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>&#160;</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>&#160;</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>&#160;</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>&#160;</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>&#160; <span class="comment">// Batch 1, Channel 3</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>&#160;</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>&#160; <span class="comment">// Batch 1, Channel 4</span></div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>&#160;</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>&#160; <span class="comment">// Batch 1, Channel 5</span></div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>&#160;</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>&#160; <span class="comment">// Batch 1, Channel 6</span></div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>&#160;</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>&#160; <span class="comment">// Batch 1, Channel 7</span></div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>&#160; 31.0f, 32.0f,</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>&#160; },</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>&#160;</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1164<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1165</div><!-- fragment -->
1166</div>
1167</div>
1168<a id="a1f8ad3cf8df29398ea04eaa4c790a100"></a>
1169<h2 class="memtitle"><span class="permalink"><a href="#a1f8ad3cf8df29398ea04eaa4c790a100">&#9670;&nbsp;</a></span>Concat3dDim1DiffInputDimsUint8Test()</h2>
1170
1171<div class="memitem">
1172<div class="memproto">
1173 <table class="memname">
1174 <tr>
1175 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; Concat3dDim1DiffInputDimsUint8Test </td>
1176 <td>(</td>
1177 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1178 <td class="paramname"><em>workloadFactory</em>, </td>
1179 </tr>
1180 <tr>
1181 <td class="paramkey"></td>
1182 <td></td>
1183 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1184 <td class="paramname"><em>memoryManager</em>&#160;</td>
1185 </tr>
1186 <tr>
1187 <td></td>
1188 <td>)</td>
1189 <td></td><td></td>
1190 </tr>
1191 </table>
1192</div><div class="memdoc">
1193
1194<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02837">2837</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1195<div class="fragment"><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>&#160;{</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>&#160; <span class="keywordflow">return</span> Concat3dDim1DiffInputDimsTestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>&#160; workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>&#160;}</div></div><!-- fragment -->
1196</div>
1197</div>
1198<a id="a462db75851b433b8739039a789e14c0f"></a>
1199<h2 class="memtitle"><span class="permalink"><a href="#a462db75851b433b8739039a789e14c0f">&#9670;&nbsp;</a></span>Concat3dDim1Test()</h2>
1200
1201<div class="memitem">
1202<div class="memproto">
1203 <table class="memname">
1204 <tr>
1205 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 3&gt; Concat3dDim1Test </td>
1206 <td>(</td>
1207 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1208 <td class="paramname"><em>workloadFactory</em>, </td>
1209 </tr>
1210 <tr>
1211 <td class="paramkey"></td>
1212 <td></td>
1213 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1214 <td class="paramname"><em>memoryManager</em>&#160;</td>
1215 </tr>
1216 <tr>
1217 <td></td>
1218 <td>)</td>
1219 <td></td><td></td>
1220 </tr>
1221 </table>
1222</div><div class="memdoc">
1223
1224<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02239">2239</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1225<div class="fragment"><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>&#160;{</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>&#160; <span class="keywordflow">return</span> Concat3dDim1TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>&#160;}</div></div><!-- fragment -->
1226</div>
1227</div>
1228<a id="a79b36066d3bbd4ce6a61c081ea863ad7"></a>
1229<h2 class="memtitle"><span class="permalink"><a href="#a79b36066d3bbd4ce6a61c081ea863ad7">&#9670;&nbsp;</a></span>Concat3dDim1TestImpl()</h2>
1230
1231<div class="memitem">
1232<div class="memproto">
1233 <table class="memname">
1234 <tr>
1235 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; Concat3dDim1TestImpl </td>
1236 <td>(</td>
1237 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1238 <td class="paramname"><em>workloadFactory</em>, </td>
1239 </tr>
1240 <tr>
1241 <td class="paramkey"></td>
1242 <td></td>
1243 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1244 <td class="paramname"><em>memoryManager</em>, </td>
1245 </tr>
1246 <tr>
1247 <td class="paramkey"></td>
1248 <td></td>
1249 <td class="paramtype">float&#160;</td>
1250 <td class="paramname"><em>qScale</em>, </td>
1251 </tr>
1252 <tr>
1253 <td class="paramkey"></td>
1254 <td></td>
1255 <td class="paramtype">int32_t&#160;</td>
1256 <td class="paramname"><em>qOffset</em>&#160;</td>
1257 </tr>
1258 <tr>
1259 <td></td>
1260 <td>)</td>
1261 <td></td><td></td>
1262 </tr>
1263 </table>
1264</div><div class="memdoc">
1265
1266<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00882">882</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1267
1268<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
1269<div class="fragment"><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>&#160;{</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 9, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>&#160;</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> result = Concat3dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 1, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>&#160;</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 3&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>&#160; {</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>&#160;</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>&#160;</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>&#160;</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>&#160; <span class="comment">// Batch 0, Channel 3</span></div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>&#160;</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>&#160; <span class="comment">// Batch 0, Channel 4</span></div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>&#160;</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>&#160; <span class="comment">// Batch 0, Channel 5</span></div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>&#160;</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>&#160; <span class="comment">// Batch 0, Channel 6</span></div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>&#160;</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>&#160; <span class="comment">// Batch 0, Channel 7</span></div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>&#160;</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>&#160; <span class="comment">// Batch 0, Channel 8</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>&#160;</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>&#160;</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>&#160;</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>&#160;</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>&#160; <span class="comment">// Batch 1, Channel 3</span></div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>&#160;</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>&#160; <span class="comment">// Batch 1, Channel 4</span></div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>&#160;</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>&#160; <span class="comment">// Batch 1, Channel 5</span></div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>&#160;</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>&#160; <span class="comment">// Batch 1, Channel 6</span></div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>&#160; 31.0f, 32.0f,</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>&#160;</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>&#160; <span class="comment">// Batch 1, Channel 7</span></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>&#160; 33.0f, 34.0f,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>&#160;</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>&#160; <span class="comment">// Batch 1, Channel 8</span></div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>&#160; 35.0f, 36.0f</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>&#160; },</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>&#160;</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1270<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
1271<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1272</div><!-- fragment -->
1273</div>
1274</div>
1275<a id="a8e409cdc677af52ce07c5cdc8ec63678"></a>
1276<h2 class="memtitle"><span class="permalink"><a href="#a8e409cdc677af52ce07c5cdc8ec63678">&#9670;&nbsp;</a></span>Concat3dDim1Uint8Test()</h2>
1277
1278<div class="memitem">
1279<div class="memproto">
1280 <table class="memname">
1281 <tr>
1282 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; Concat3dDim1Uint8Test </td>
1283 <td>(</td>
1284 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1285 <td class="paramname"><em>workloadFactory</em>, </td>
1286 </tr>
1287 <tr>
1288 <td class="paramkey"></td>
1289 <td></td>
1290 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1291 <td class="paramname"><em>memoryManager</em>&#160;</td>
1292 </tr>
1293 <tr>
1294 <td></td>
1295 <td>)</td>
1296 <td></td><td></td>
1297 </tr>
1298 </table>
1299</div><div class="memdoc">
1300
1301<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02814">2814</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1302<div class="fragment"><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>&#160;{</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>&#160; <span class="keywordflow">return</span> Concat3dDim1TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>&#160;}</div></div><!-- fragment -->
1303</div>
1304</div>
1305<a id="aab6fb09abdae83f7944da4d9d8a894de"></a>
1306<h2 class="memtitle"><span class="permalink"><a href="#aab6fb09abdae83f7944da4d9d8a894de">&#9670;&nbsp;</a></span>Concat3dDim2DiffInputDimsTest()</h2>
1307
1308<div class="memitem">
1309<div class="memproto">
1310 <table class="memname">
1311 <tr>
1312 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 3&gt; Concat3dDim2DiffInputDimsTest </td>
1313 <td>(</td>
1314 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1315 <td class="paramname"><em>workloadFactory</em>, </td>
1316 </tr>
1317 <tr>
1318 <td class="paramkey"></td>
1319 <td></td>
1320 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1321 <td class="paramname"><em>memoryManager</em>, </td>
1322 </tr>
1323 <tr>
1324 <td class="paramkey"></td>
1325 <td></td>
1326 <td class="paramtype">bool&#160;</td>
1327 <td class="paramname"><em>useSubtensor</em>&#160;</td>
1328 </tr>
1329 <tr>
1330 <td></td>
1331 <td>)</td>
1332 <td></td><td></td>
1333 </tr>
1334 </table>
1335</div><div class="memdoc">
1336
1337<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02269">2269</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1338<div class="fragment"><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>&#160;{</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>&#160; <span class="keywordflow">return</span> Concat3dDim2DiffInputDimsTestImpl&lt;DataType::Float32&gt;(</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>&#160; workloadFactory, memoryManager, useSubtensor, 0.0f, 0);</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>&#160;}</div></div><!-- fragment -->
1339</div>
1340</div>
1341<a id="a8af1d375ac13d009cf818825b343ec1c"></a>
1342<h2 class="memtitle"><span class="permalink"><a href="#a8af1d375ac13d009cf818825b343ec1c">&#9670;&nbsp;</a></span>Concat3dDim2DiffInputDimsTestImpl()</h2>
1343
1344<div class="memitem">
1345<div class="memproto">
1346 <table class="memname">
1347 <tr>
1348 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; Concat3dDim2DiffInputDimsTestImpl </td>
1349 <td>(</td>
1350 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1351 <td class="paramname"><em>workloadFactory</em>, </td>
1352 </tr>
1353 <tr>
1354 <td class="paramkey"></td>
1355 <td></td>
1356 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1357 <td class="paramname"><em>memoryManager</em>, </td>
1358 </tr>
1359 <tr>
1360 <td class="paramkey"></td>
1361 <td></td>
1362 <td class="paramtype">bool&#160;</td>
1363 <td class="paramname"><em>useSubtensor</em>, </td>
1364 </tr>
1365 <tr>
1366 <td class="paramkey"></td>
1367 <td></td>
1368 <td class="paramtype">float&#160;</td>
1369 <td class="paramname"><em>qScale</em>, </td>
1370 </tr>
1371 <tr>
1372 <td class="paramkey"></td>
1373 <td></td>
1374 <td class="paramtype">int32_t&#160;</td>
1375 <td class="paramname"><em>qOffset</em>&#160;</td>
1376 </tr>
1377 <tr>
1378 <td></td>
1379 <td>)</td>
1380 <td></td><td></td>
1381 </tr>
1382 </table>
1383</div><div class="memdoc">
1384
1385<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01283">1283</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1386<div class="fragment"><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>&#160;{</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 3&gt;(input0TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>&#160; {</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>&#160;</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>&#160;</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>&#160;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>&#160;</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>&#160;</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>&#160; 23.0f, 24.0f</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>&#160; },</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>&#160;</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 2, 3, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 3&gt;(input1TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>&#160; {</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>&#160; 7.0f,</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>&#160;</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>&#160; 9.0f,</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>&#160;</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>&#160; 11.0f,</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>&#160;</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>&#160; 25.0f,</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>&#160;</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>&#160; 27.0f,</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>&#160;</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>&#160; 29.0f</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>&#160; },</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>&#160;</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 2, 3, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 3&gt;(input2TensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>&#160; {</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>&#160; 13.0f, 14.0f, 50.0f,</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>&#160;</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>&#160; 15.0f, 16.0f, 51.0f,</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>&#160;</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>&#160; 17.0f, 18.0f, 52.0f,</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>&#160;</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>&#160; 31.0f, 32.0f, 53.0f,</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>&#160;</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>&#160; 33.0f, 34.0f, 54.0f,</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>&#160;</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>&#160; 35.0f, 36.0f, 55.0f,</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>&#160; },</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>&#160;</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>&#160;</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>&#160; { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>&#160; output.data(),</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>&#160; 2,</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>&#160; useSubtensor);</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>&#160;</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>&#160; result.output = MakeTensor&lt;T, 3&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>&#160; result.outputExpected = MakeTensor&lt;T, 3&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>&#160; {</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>&#160; 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f,</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>&#160;</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>&#160; 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f,</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>&#160;</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>&#160; 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f,</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>&#160;</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>&#160; 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f,</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>&#160;</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>&#160; 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f,</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>&#160;</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>&#160; 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f,</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>&#160; },</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>&#160;</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1387<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1388</div><!-- fragment -->
1389</div>
1390</div>
1391<a id="a6a0578f5cabc3b13c8800066d094f08b"></a>
1392<h2 class="memtitle"><span class="permalink"><a href="#a6a0578f5cabc3b13c8800066d094f08b">&#9670;&nbsp;</a></span>Concat3dDim2DiffInputDimsUint8Test()</h2>
1393
1394<div class="memitem">
1395<div class="memproto">
1396 <table class="memname">
1397 <tr>
1398 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; Concat3dDim2DiffInputDimsUint8Test </td>
1399 <td>(</td>
1400 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1401 <td class="paramname"><em>workloadFactory</em>, </td>
1402 </tr>
1403 <tr>
1404 <td class="paramkey"></td>
1405 <td></td>
1406 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1407 <td class="paramname"><em>memoryManager</em>, </td>
1408 </tr>
1409 <tr>
1410 <td class="paramkey"></td>
1411 <td></td>
1412 <td class="paramtype">bool&#160;</td>
1413 <td class="paramname"><em>useSubtensor</em>&#160;</td>
1414 </tr>
1415 <tr>
1416 <td></td>
1417 <td>)</td>
1418 <td></td><td></td>
1419 </tr>
1420 </table>
1421</div><div class="memdoc">
1422
1423<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02845">2845</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1424<div class="fragment"><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>&#160;{</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span>&#160; <span class="keywordflow">return</span> Concat3dDim2DiffInputDimsTestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>&#160; workloadFactory, memoryManager, useSubtensor, 0.5f, -1);</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>&#160;}</div></div><!-- fragment -->
1425</div>
1426</div>
1427<a id="ade318c9975477ee7bab3d230baf8d48a"></a>
1428<h2 class="memtitle"><span class="permalink"><a href="#ade318c9975477ee7bab3d230baf8d48a">&#9670;&nbsp;</a></span>Concat3dDim2Test()</h2>
1429
1430<div class="memitem">
1431<div class="memproto">
1432 <table class="memname">
1433 <tr>
1434 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 3&gt; Concat3dDim2Test </td>
1435 <td>(</td>
1436 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1437 <td class="paramname"><em>workloadFactory</em>, </td>
1438 </tr>
1439 <tr>
1440 <td class="paramkey"></td>
1441 <td></td>
1442 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1443 <td class="paramname"><em>memoryManager</em>, </td>
1444 </tr>
1445 <tr>
1446 <td class="paramkey"></td>
1447 <td></td>
1448 <td class="paramtype">bool&#160;</td>
1449 <td class="paramname"><em>useSubtensor</em>&#160;</td>
1450 </tr>
1451 <tr>
1452 <td></td>
1453 <td>)</td>
1454 <td></td><td></td>
1455 </tr>
1456 </table>
1457</div><div class="memdoc">
1458
1459<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02246">2246</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1460<div class="fragment"><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>&#160;{</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>&#160; <span class="keywordflow">return</span> Concat3dDim2TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, useSubtensor, 0.0f, 0);</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>&#160;}</div></div><!-- fragment -->
1461</div>
1462</div>
1463<a id="a89188ab52e61bc27b6e6bc4ccc81a413"></a>
1464<h2 class="memtitle"><span class="permalink"><a href="#a89188ab52e61bc27b6e6bc4ccc81a413">&#9670;&nbsp;</a></span>Concat3dDim2TestImpl()</h2>
1465
1466<div class="memitem">
1467<div class="memproto">
1468 <table class="memname">
1469 <tr>
1470 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; Concat3dDim2TestImpl </td>
1471 <td>(</td>
1472 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1473 <td class="paramname"><em>workloadFactory</em>, </td>
1474 </tr>
1475 <tr>
1476 <td class="paramkey"></td>
1477 <td></td>
1478 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1479 <td class="paramname"><em>memoryManager</em>, </td>
1480 </tr>
1481 <tr>
1482 <td class="paramkey"></td>
1483 <td></td>
1484 <td class="paramtype">bool&#160;</td>
1485 <td class="paramname"><em>useSubtensor</em>, </td>
1486 </tr>
1487 <tr>
1488 <td class="paramkey"></td>
1489 <td></td>
1490 <td class="paramtype">float&#160;</td>
1491 <td class="paramname"><em>qScale</em>, </td>
1492 </tr>
1493 <tr>
1494 <td class="paramkey"></td>
1495 <td></td>
1496 <td class="paramtype">int32_t&#160;</td>
1497 <td class="paramname"><em>qOffset</em>&#160;</td>
1498 </tr>
1499 <tr>
1500 <td></td>
1501 <td>)</td>
1502 <td></td><td></td>
1503 </tr>
1504 </table>
1505</div><div class="memdoc">
1506
1507<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00955">955</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1508
1509<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
1510<div class="fragment"><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>&#160;{</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>&#160;</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> result = Concat3dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 2, useSubtensor, qScale, qOffset);</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>&#160;</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 3&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>&#160; {</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>&#160; 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>&#160;</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>&#160; 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f,</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>&#160;</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>&#160; 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>&#160;</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>&#160; 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f,</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>&#160;</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>&#160; 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f,</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>&#160;</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>&#160; 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f,</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>&#160; },</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>&#160;</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1511<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
1512<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1513</div><!-- fragment -->
1514</div>
1515</div>
1516<a id="a75091ca6eb52deea2ce14ad8f6261236"></a>
1517<h2 class="memtitle"><span class="permalink"><a href="#a75091ca6eb52deea2ce14ad8f6261236">&#9670;&nbsp;</a></span>Concat3dDim2Uint8Test()</h2>
1518
1519<div class="memitem">
1520<div class="memproto">
1521 <table class="memname">
1522 <tr>
1523 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; Concat3dDim2Uint8Test </td>
1524 <td>(</td>
1525 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1526 <td class="paramname"><em>workloadFactory</em>, </td>
1527 </tr>
1528 <tr>
1529 <td class="paramkey"></td>
1530 <td></td>
1531 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1532 <td class="paramname"><em>memoryManager</em>, </td>
1533 </tr>
1534 <tr>
1535 <td class="paramkey"></td>
1536 <td></td>
1537 <td class="paramtype">bool&#160;</td>
1538 <td class="paramname"><em>useSubtensor</em>&#160;</td>
1539 </tr>
1540 <tr>
1541 <td></td>
1542 <td>)</td>
1543 <td></td><td></td>
1544 </tr>
1545 </table>
1546</div><div class="memdoc">
1547
1548<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02821">2821</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1549<div class="fragment"><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>&#160;{</div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>&#160; <span class="keywordflow">return</span> Concat3dDim2TestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>&#160; workloadFactory, memoryManager, useSubtensor, 0.5f, -1);</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>&#160;}</div></div><!-- fragment -->
1550</div>
1551</div>
1552<a id="a7fbe775cdbc1967d651a97702a0eb08f"></a>
1553<h2 class="memtitle"><span class="permalink"><a href="#a7fbe775cdbc1967d651a97702a0eb08f">&#9670;&nbsp;</a></span>Concat3dTestImpl()</h2>
1554
1555<div class="memitem">
1556<div class="memproto">
1557 <table class="memname">
1558 <tr>
1559 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; Concat3dTestImpl </td>
1560 <td>(</td>
1561 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1562 <td class="paramname"><em>workloadFactory</em>, </td>
1563 </tr>
1564 <tr>
1565 <td class="paramkey"></td>
1566 <td></td>
1567 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1568 <td class="paramname"><em>memoryManager</em>, </td>
1569 </tr>
1570 <tr>
1571 <td class="paramkey"></td>
1572 <td></td>
1573 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;&#160;</td>
1574 <td class="paramname"><em>outputTensorInfo</em>, </td>
1575 </tr>
1576 <tr>
1577 <td class="paramkey"></td>
1578 <td></td>
1579 <td class="paramtype">unsigned int&#160;</td>
1580 <td class="paramname"><em>dimension</em>, </td>
1581 </tr>
1582 <tr>
1583 <td class="paramkey"></td>
1584 <td></td>
1585 <td class="paramtype">bool&#160;</td>
1586 <td class="paramname"><em>useSubtensor</em>, </td>
1587 </tr>
1588 <tr>
1589 <td class="paramkey"></td>
1590 <td></td>
1591 <td class="paramtype">float&#160;</td>
1592 <td class="paramname"><em>qScale</em>, </td>
1593 </tr>
1594 <tr>
1595 <td class="paramkey"></td>
1596 <td></td>
1597 <td class="paramtype">int32_t&#160;</td>
1598 <td class="paramname"><em>qOffset</em>&#160;</td>
1599 </tr>
1600 <tr>
1601 <td></td>
1602 <td>)</td>
1603 <td></td><td></td>
1604 </tr>
1605 </table>
1606</div><div class="memdoc">
1607
1608<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00715">715</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1609
1610<p class="reference">References <a class="el" href="_tensor_8hpp_source.xhtml#l00093">TensorInfo::GetNumElements()</a>, and <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>.</p>
1611<div class="fragment"><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>&#160;{</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>&#160;</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 3&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>&#160; {</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>&#160;</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>&#160;</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>&#160;</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>&#160;</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>&#160;</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>&#160; 23.0f, 24.0f</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>&#160; },</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>&#160;</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 3&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>&#160; {</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>&#160;</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>&#160;</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>&#160;</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>&#160;</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>&#160;</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>&#160; 29.0f, 30.0f</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>&#160; },</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>&#160;</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 3&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>&#160; {</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>&#160; <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>&#160;</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>&#160; <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>&#160;</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>&#160; <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>&#160;</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>&#160; <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>&#160; 31.0f, 32.0f,</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>&#160;</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>&#160; <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>&#160; 33.0f, 34.0f,</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>&#160;</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>&#160; <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>&#160; 35.0f, 36.0f</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>&#160; },</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>&#160;</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>&#160;</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>&#160; output.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>&#160; Concatenate&lt;T&gt;(workloadFactory, memoryManager,</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>&#160; { inputTensorInfo, inputTensorInfo, inputTensorInfo },</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>&#160; { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>&#160; output.data(),</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>&#160; dimension,</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>&#160; useSubtensor);</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>&#160;</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>&#160; result.output = MakeTensor&lt;T, 3&gt;(outputTensorInfo, output);</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1612<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1613<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00093">Tensor.hpp:93</a></div></div>
1614</div><!-- fragment -->
1615</div>
1616</div>
1617<a id="a9199f32df2745143e544e703c2380dd4"></a>
1618<h2 class="memtitle"><span class="permalink"><a href="#a9199f32df2745143e544e703c2380dd4">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim0Test()</h2>
1619
1620<div class="memitem">
1621<div class="memproto">
1622 <table class="memname">
1623 <tr>
1624 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDiffShapeDim0Test </td>
1625 <td>(</td>
1626 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1627 <td class="paramname"><em>workloadFactory</em>, </td>
1628 </tr>
1629 <tr>
1630 <td class="paramkey"></td>
1631 <td></td>
1632 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1633 <td class="paramname"><em>memoryManager</em>&#160;</td>
1634 </tr>
1635 <tr>
1636 <td></td>
1637 <td>)</td>
1638 <td></td><td></td>
1639 </tr>
1640 </table>
1641</div><div class="memdoc">
1642
1643<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02307">2307</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1644<div class="fragment"><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>&#160;{</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim0TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>&#160;}</div></div><!-- fragment -->
1645</div>
1646</div>
1647<a id="a00d88e24db4f4af21b6ba36d206a296c"></a>
1648<h2 class="memtitle"><span class="permalink"><a href="#a00d88e24db4f4af21b6ba36d206a296c">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim0TestImpl()</h2>
1649
1650<div class="memitem">
1651<div class="memproto">
1652 <table class="memname">
1653 <tr>
1654 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDiffShapeDim0TestImpl </td>
1655 <td>(</td>
1656 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1657 <td class="paramname"><em>workloadFactory</em>, </td>
1658 </tr>
1659 <tr>
1660 <td class="paramkey"></td>
1661 <td></td>
1662 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1663 <td class="paramname"><em>memoryManager</em>, </td>
1664 </tr>
1665 <tr>
1666 <td class="paramkey"></td>
1667 <td></td>
1668 <td class="paramtype">float&#160;</td>
1669 <td class="paramname"><em>qScale</em>, </td>
1670 </tr>
1671 <tr>
1672 <td class="paramkey"></td>
1673 <td></td>
1674 <td class="paramtype">int32_t&#160;</td>
1675 <td class="paramname"><em>qOffset</em>&#160;</td>
1676 </tr>
1677 <tr>
1678 <td></td>
1679 <td>)</td>
1680 <td></td><td></td>
1681 </tr>
1682 </table>
1683</div><div class="memdoc">
1684
1685<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01623">1623</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1686<div class="fragment"><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>&#160;{</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 0u;</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>&#160;</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 4&gt;(inputTensorInfo0, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>&#160; {</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>&#160; 11.0f, 12.0f</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>&#160; },</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>&#160;</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>&#160;</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 4&gt;(inputTensorInfo1, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>&#160; {</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>&#160;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>&#160; 31.0f, 32.0f</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>&#160; },</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>&#160;</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>&#160;</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>&#160;</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>&#160; Concatenate&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>&#160; memoryManager,</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>&#160; {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>&#160; {input0.data(), input1.data()},</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>&#160; output.data(),</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>&#160; dimension,</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>&#160;</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>&#160; result.output = MakeTensor&lt;T, 4&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>&#160; result.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>&#160; {</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>&#160;</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>&#160;</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>&#160; 31.0f, 32.0f</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>&#160; },</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>&#160;</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1687<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1688</div><!-- fragment -->
1689</div>
1690</div>
1691<a id="a39a5321f36681cf1b7bbea885a0ccce9"></a>
1692<h2 class="memtitle"><span class="permalink"><a href="#a39a5321f36681cf1b7bbea885a0ccce9">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim0Uint8Test()</h2>
1693
1694<div class="memitem">
1695<div class="memproto">
1696 <table class="memname">
1697 <tr>
1698 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDiffShapeDim0Uint8Test </td>
1699 <td>(</td>
1700 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1701 <td class="paramname"><em>workloadFactory</em>, </td>
1702 </tr>
1703 <tr>
1704 <td class="paramkey"></td>
1705 <td></td>
1706 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1707 <td class="paramname"><em>memoryManager</em>&#160;</td>
1708 </tr>
1709 <tr>
1710 <td></td>
1711 <td>)</td>
1712 <td></td><td></td>
1713 </tr>
1714 </table>
1715</div><div class="memdoc">
1716
1717<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02883">2883</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1718<div class="fragment"><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span>&#160;{</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim0TestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>&#160; workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>&#160;}</div></div><!-- fragment -->
1719</div>
1720</div>
1721<a id="aa40068e0a65840e70b2da4902a0f47da"></a>
1722<h2 class="memtitle"><span class="permalink"><a href="#aa40068e0a65840e70b2da4902a0f47da">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim1Test()</h2>
1723
1724<div class="memitem">
1725<div class="memproto">
1726 <table class="memname">
1727 <tr>
1728 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDiffShapeDim1Test </td>
1729 <td>(</td>
1730 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1731 <td class="paramname"><em>workloadFactory</em>, </td>
1732 </tr>
1733 <tr>
1734 <td class="paramkey"></td>
1735 <td></td>
1736 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1737 <td class="paramname"><em>memoryManager</em>&#160;</td>
1738 </tr>
1739 <tr>
1740 <td></td>
1741 <td>)</td>
1742 <td></td><td></td>
1743 </tr>
1744 </table>
1745</div><div class="memdoc">
1746
1747<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02314">2314</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1748<div class="fragment"><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>&#160;{</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim1TestImpl&lt;DataType::Float32&gt;(</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>&#160; workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span>&#160;}</div></div><!-- fragment -->
1749</div>
1750</div>
1751<a id="afca22d4151120b94ca2c68c662193cc1"></a>
1752<h2 class="memtitle"><span class="permalink"><a href="#afca22d4151120b94ca2c68c662193cc1">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim1TestImpl()</h2>
1753
1754<div class="memitem">
1755<div class="memproto">
1756 <table class="memname">
1757 <tr>
1758 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDiffShapeDim1TestImpl </td>
1759 <td>(</td>
1760 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1761 <td class="paramname"><em>workloadFactory</em>, </td>
1762 </tr>
1763 <tr>
1764 <td class="paramkey"></td>
1765 <td></td>
1766 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1767 <td class="paramname"><em>memoryManager</em>, </td>
1768 </tr>
1769 <tr>
1770 <td class="paramkey"></td>
1771 <td></td>
1772 <td class="paramtype">float&#160;</td>
1773 <td class="paramname"><em>qScale</em>, </td>
1774 </tr>
1775 <tr>
1776 <td class="paramkey"></td>
1777 <td></td>
1778 <td class="paramtype">int32_t&#160;</td>
1779 <td class="paramname"><em>qOffset</em>&#160;</td>
1780 </tr>
1781 <tr>
1782 <td></td>
1783 <td>)</td>
1784 <td></td><td></td>
1785 </tr>
1786 </table>
1787</div><div class="memdoc">
1788
1789<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01708">1708</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1790<div class="fragment"><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>&#160;{</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 1u;</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>&#160;</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 4&gt;(inputTensorInfo0, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>&#160; {</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>&#160; 11.0f, 12.0f</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>&#160; },</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>&#160;</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>&#160;</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 4&gt;(inputTensorInfo1, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>&#160; {</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>&#160; },</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>&#160;</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>&#160;</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>&#160;</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>&#160; Concatenate&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>&#160; memoryManager,</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>&#160; {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>&#160; {input0.data(), input1.data()},</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>&#160; output.data(),</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>&#160; dimension,</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>&#160;</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>&#160; result.output = MakeTensor&lt;T, 4&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>&#160; result.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>&#160; {</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>&#160; 17.0f, 18.0f</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>&#160; },</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>&#160;</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1791<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1792</div><!-- fragment -->
1793</div>
1794</div>
1795<a id="af5b51da08139262f68be752047e1b94c"></a>
1796<h2 class="memtitle"><span class="permalink"><a href="#af5b51da08139262f68be752047e1b94c">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim1Uint8Test()</h2>
1797
1798<div class="memitem">
1799<div class="memproto">
1800 <table class="memname">
1801 <tr>
1802 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDiffShapeDim1Uint8Test </td>
1803 <td>(</td>
1804 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1805 <td class="paramname"><em>workloadFactory</em>, </td>
1806 </tr>
1807 <tr>
1808 <td class="paramkey"></td>
1809 <td></td>
1810 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1811 <td class="paramname"><em>memoryManager</em>&#160;</td>
1812 </tr>
1813 <tr>
1814 <td></td>
1815 <td>)</td>
1816 <td></td><td></td>
1817 </tr>
1818 </table>
1819</div><div class="memdoc">
1820
1821<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02891">2891</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1822<div class="fragment"><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>&#160;{</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim1TestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>&#160; workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>&#160;}</div></div><!-- fragment -->
1823</div>
1824</div>
1825<a id="ab7261b2e00a06881f0c8bf3e2ecbff19"></a>
1826<h2 class="memtitle"><span class="permalink"><a href="#ab7261b2e00a06881f0c8bf3e2ecbff19">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim2Test()</h2>
1827
1828<div class="memitem">
1829<div class="memproto">
1830 <table class="memname">
1831 <tr>
1832 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDiffShapeDim2Test </td>
1833 <td>(</td>
1834 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1835 <td class="paramname"><em>workloadFactory</em>, </td>
1836 </tr>
1837 <tr>
1838 <td class="paramkey"></td>
1839 <td></td>
1840 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1841 <td class="paramname"><em>memoryManager</em>&#160;</td>
1842 </tr>
1843 <tr>
1844 <td></td>
1845 <td>)</td>
1846 <td></td><td></td>
1847 </tr>
1848 </table>
1849</div><div class="memdoc">
1850
1851<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02322">2322</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1852<div class="fragment"><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>&#160;{</div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim2TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>&#160;}</div></div><!-- fragment -->
1853</div>
1854</div>
1855<a id="a75ce8fbfdee084faa855d8e61d09b56d"></a>
1856<h2 class="memtitle"><span class="permalink"><a href="#a75ce8fbfdee084faa855d8e61d09b56d">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim2TestImpl()</h2>
1857
1858<div class="memitem">
1859<div class="memproto">
1860 <table class="memname">
1861 <tr>
1862 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDiffShapeDim2TestImpl </td>
1863 <td>(</td>
1864 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1865 <td class="paramname"><em>workloadFactory</em>, </td>
1866 </tr>
1867 <tr>
1868 <td class="paramkey"></td>
1869 <td></td>
1870 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1871 <td class="paramname"><em>memoryManager</em>, </td>
1872 </tr>
1873 <tr>
1874 <td class="paramkey"></td>
1875 <td></td>
1876 <td class="paramtype">float&#160;</td>
1877 <td class="paramname"><em>qScale</em>, </td>
1878 </tr>
1879 <tr>
1880 <td class="paramkey"></td>
1881 <td></td>
1882 <td class="paramtype">int32_t&#160;</td>
1883 <td class="paramname"><em>qOffset</em>&#160;</td>
1884 </tr>
1885 <tr>
1886 <td></td>
1887 <td>)</td>
1888 <td></td><td></td>
1889 </tr>
1890 </table>
1891</div><div class="memdoc">
1892
1893<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01774">1774</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1894<div class="fragment"><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>&#160;{</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 2u;</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>&#160;</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 4&gt;(inputTensorInfo0, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>&#160; {</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>&#160; 11.0f, 12.0f</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>&#160; },</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>&#160;</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 4&gt;(inputTensorInfo1, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>&#160; {</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>&#160; 27.0f, 28.0f</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>&#160; },</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>&#160;</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>&#160;</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>&#160; Concatenate&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>&#160; memoryManager,</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>&#160; {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>&#160; {input0.data(), input1.data()},</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>&#160; output.data(),</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>&#160; dimension,</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>&#160; <span class="keyword">true</span>);</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>&#160;</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>&#160; result.output = MakeTensor&lt;T, 4&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>&#160; result.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>&#160; {</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>&#160;</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>&#160;</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>&#160; 27.0f, 28.0f</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>&#160; },</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>&#160;</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
1895<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
1896</div><!-- fragment -->
1897</div>
1898</div>
1899<a id="a4ab1a7c2b554de49ef453e802eaf88a3"></a>
1900<h2 class="memtitle"><span class="permalink"><a href="#a4ab1a7c2b554de49ef453e802eaf88a3">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim2Uint8Test()</h2>
1901
1902<div class="memitem">
1903<div class="memproto">
1904 <table class="memname">
1905 <tr>
1906 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDiffShapeDim2Uint8Test </td>
1907 <td>(</td>
1908 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1909 <td class="paramname"><em>workloadFactory</em>, </td>
1910 </tr>
1911 <tr>
1912 <td class="paramkey"></td>
1913 <td></td>
1914 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1915 <td class="paramname"><em>memoryManager</em>&#160;</td>
1916 </tr>
1917 <tr>
1918 <td></td>
1919 <td>)</td>
1920 <td></td><td></td>
1921 </tr>
1922 </table>
1923</div><div class="memdoc">
1924
1925<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02899">2899</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1926<div class="fragment"><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>&#160;{</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim2TestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span>&#160; workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span>&#160;}</div></div><!-- fragment -->
1927</div>
1928</div>
1929<a id="a6323bb2aa7e5a8215d1c38e7e0159d29"></a>
1930<h2 class="memtitle"><span class="permalink"><a href="#a6323bb2aa7e5a8215d1c38e7e0159d29">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim3Test()</h2>
1931
1932<div class="memitem">
1933<div class="memproto">
1934 <table class="memname">
1935 <tr>
1936 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDiffShapeDim3Test </td>
1937 <td>(</td>
1938 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1939 <td class="paramname"><em>workloadFactory</em>, </td>
1940 </tr>
1941 <tr>
1942 <td class="paramkey"></td>
1943 <td></td>
1944 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1945 <td class="paramname"><em>memoryManager</em>, </td>
1946 </tr>
1947 <tr>
1948 <td class="paramkey"></td>
1949 <td></td>
1950 <td class="paramtype">bool&#160;</td>
1951 <td class="paramname"><em>useSubtensor</em>&#160;</td>
1952 </tr>
1953 <tr>
1954 <td></td>
1955 <td>)</td>
1956 <td></td><td></td>
1957 </tr>
1958 </table>
1959</div><div class="memdoc">
1960
1961<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02329">2329</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
1962<div class="fragment"><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>&#160;{</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim3TestImpl&lt;DataType::Float32&gt;(</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>&#160; workloadFactory, memoryManager, 0.0f, 0, useSubtensor);</div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>&#160;}</div></div><!-- fragment -->
1963</div>
1964</div>
1965<a id="a6318384f0f00e73bd26e43b7c4ca7735"></a>
1966<h2 class="memtitle"><span class="permalink"><a href="#a6318384f0f00e73bd26e43b7c4ca7735">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim3TestImpl()</h2>
1967
1968<div class="memitem">
1969<div class="memproto">
1970 <table class="memname">
1971 <tr>
1972 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDiffShapeDim3TestImpl </td>
1973 <td>(</td>
1974 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
1975 <td class="paramname"><em>workloadFactory</em>, </td>
1976 </tr>
1977 <tr>
1978 <td class="paramkey"></td>
1979 <td></td>
1980 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1981 <td class="paramname"><em>memoryManager</em>, </td>
1982 </tr>
1983 <tr>
1984 <td class="paramkey"></td>
1985 <td></td>
1986 <td class="paramtype">float&#160;</td>
1987 <td class="paramname"><em>qScale</em>, </td>
1988 </tr>
1989 <tr>
1990 <td class="paramkey"></td>
1991 <td></td>
1992 <td class="paramtype">int32_t&#160;</td>
1993 <td class="paramname"><em>qOffset</em>, </td>
1994 </tr>
1995 <tr>
1996 <td class="paramkey"></td>
1997 <td></td>
1998 <td class="paramtype">bool&#160;</td>
1999 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2000 </tr>
2001 <tr>
2002 <td></td>
2003 <td>)</td>
2004 <td></td><td></td>
2005 </tr>
2006 </table>
2007</div><div class="memdoc">
2008
2009<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01850">1850</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2010<div class="fragment"><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>&#160;{</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 3u;</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>&#160;</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 4&gt;(inputTensorInfo0, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>&#160; {</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>&#160; 11.0f, 12.0f</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>&#160; },</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>&#160;</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 4&gt;(inputTensorInfo1, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>&#160; {</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>&#160; 11.0f, 12.0f, 13.0f,</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>&#160; 14.0f, 15.0f, 16.0f,</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>&#160;</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>&#160; 17.0f, 18.0f, 19.0f,</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>&#160; 20.0f, 21.0f, 22.0f,</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>&#160;</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>&#160; 23.0f, 24.0f, 25.0f,</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>&#160; 26.0f, 27.0f, 28.0f</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>&#160; },</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>&#160;</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>&#160;</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>&#160;</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>&#160; output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>&#160; Concatenate&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>&#160; memoryManager,</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>&#160; {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>&#160; {input0.data(), input1.data()},</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>&#160; output.data(),</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>&#160; dimension,</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>&#160; useSubtensor);</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>&#160;</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>&#160; result.output = MakeTensor&lt;T, 4&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>&#160; result.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>&#160; {</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>&#160; 1.0f, 2.0f, 11.0f, 12.0f, 13.0f,</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>&#160; 3.0f, 4.0f, 14.0f, 15.0f, 16.0f,</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>&#160; 5.0f, 6.0f, 17.0f, 18.0f, 19.0f,</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>&#160; 7.0f, 8.0f, 20.0f, 21.0f, 22.0f,</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>&#160; 9.0f, 10.0f, 23.0f, 24.0f, 25.0f,</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160; 11.0f, 12.0f, 26.0f, 27.0f, 28.0f</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>&#160; },</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160;</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2011<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2012</div><!-- fragment -->
2013</div>
2014</div>
2015<a id="a6852f3bb0b5a59260e0f76031e64cb3e"></a>
2016<h2 class="memtitle"><span class="permalink"><a href="#a6852f3bb0b5a59260e0f76031e64cb3e">&#9670;&nbsp;</a></span>Concat4dDiffShapeDim3Uint8Test()</h2>
2017
2018<div class="memitem">
2019<div class="memproto">
2020 <table class="memname">
2021 <tr>
2022 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDiffShapeDim3Uint8Test </td>
2023 <td>(</td>
2024 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2025 <td class="paramname"><em>workloadFactory</em>, </td>
2026 </tr>
2027 <tr>
2028 <td class="paramkey"></td>
2029 <td></td>
2030 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2031 <td class="paramname"><em>memoryManager</em>, </td>
2032 </tr>
2033 <tr>
2034 <td class="paramkey"></td>
2035 <td></td>
2036 <td class="paramtype">bool&#160;</td>
2037 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2038 </tr>
2039 <tr>
2040 <td></td>
2041 <td>)</td>
2042 <td></td><td></td>
2043 </tr>
2044 </table>
2045</div><div class="memdoc">
2046
2047<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02907">2907</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2048<div class="fragment"><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span>&#160;{</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>&#160; <span class="keywordflow">return</span> Concat4dDiffShapeDim3TestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>&#160; workloadFactory, memoryManager, 0.5f, -1, useSubtensor);</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>&#160;}</div></div><!-- fragment -->
2049</div>
2050</div>
2051<a id="a7f29312851dee5f74ed0bffebd5448d2"></a>
2052<h2 class="memtitle"><span class="permalink"><a href="#a7f29312851dee5f74ed0bffebd5448d2">&#9670;&nbsp;</a></span>Concat4dDim0Test()</h2>
2053
2054<div class="memitem">
2055<div class="memproto">
2056 <table class="memname">
2057 <tr>
2058 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDim0Test </td>
2059 <td>(</td>
2060 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2061 <td class="paramname"><em>workloadFactory</em>, </td>
2062 </tr>
2063 <tr>
2064 <td class="paramkey"></td>
2065 <td></td>
2066 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2067 <td class="paramname"><em>memoryManager</em>&#160;</td>
2068 </tr>
2069 <tr>
2070 <td></td>
2071 <td>)</td>
2072 <td></td><td></td>
2073 </tr>
2074 </table>
2075</div><div class="memdoc">
2076
2077<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02278">2278</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2078<div class="fragment"><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span>&#160;{</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>&#160; <span class="keywordflow">return</span> Concat4dDim0TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>&#160;}</div></div><!-- fragment -->
2079</div>
2080</div>
2081<a id="a59d4515193d877da62a352fc299d6d0f"></a>
2082<h2 class="memtitle"><span class="permalink"><a href="#a59d4515193d877da62a352fc299d6d0f">&#9670;&nbsp;</a></span>Concat4dDim0TestImpl()</h2>
2083
2084<div class="memitem">
2085<div class="memproto">
2086 <table class="memname">
2087 <tr>
2088 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDim0TestImpl </td>
2089 <td>(</td>
2090 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2091 <td class="paramname"><em>workloadFactory</em>, </td>
2092 </tr>
2093 <tr>
2094 <td class="paramkey"></td>
2095 <td></td>
2096 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2097 <td class="paramname"><em>memoryManager</em>, </td>
2098 </tr>
2099 <tr>
2100 <td class="paramkey"></td>
2101 <td></td>
2102 <td class="paramtype">float&#160;</td>
2103 <td class="paramname"><em>qScale</em>, </td>
2104 </tr>
2105 <tr>
2106 <td class="paramkey"></td>
2107 <td></td>
2108 <td class="paramtype">int32_t&#160;</td>
2109 <td class="paramname"><em>qOffset</em>&#160;</td>
2110 </tr>
2111 <tr>
2112 <td></td>
2113 <td>)</td>
2114 <td></td><td></td>
2115 </tr>
2116 </table>
2117</div><div class="memdoc">
2118
2119<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01462">1462</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2120
2121<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
2122<div class="fragment"><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>&#160;{</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>&#160;</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result = Concat4dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 0, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>&#160;</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>&#160; {</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>&#160;</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>&#160;</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>&#160; 31.0f, 32.0f</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>&#160; },</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>&#160;</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2123<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
2124<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2125</div><!-- fragment -->
2126</div>
2127</div>
2128<a id="a7b3adb97b81ab7b464c566caa3a231ba"></a>
2129<h2 class="memtitle"><span class="permalink"><a href="#a7b3adb97b81ab7b464c566caa3a231ba">&#9670;&nbsp;</a></span>Concat4dDim0Uint8Test()</h2>
2130
2131<div class="memitem">
2132<div class="memproto">
2133 <table class="memname">
2134 <tr>
2135 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDim0Uint8Test </td>
2136 <td>(</td>
2137 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2138 <td class="paramname"><em>workloadFactory</em>, </td>
2139 </tr>
2140 <tr>
2141 <td class="paramkey"></td>
2142 <td></td>
2143 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2144 <td class="paramname"><em>memoryManager</em>&#160;</td>
2145 </tr>
2146 <tr>
2147 <td></td>
2148 <td>)</td>
2149 <td></td><td></td>
2150 </tr>
2151 </table>
2152</div><div class="memdoc">
2153
2154<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02854">2854</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2155<div class="fragment"><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>&#160;{</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span>&#160; <span class="keywordflow">return</span> Concat4dDim0TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>&#160;}</div></div><!-- fragment -->
2156</div>
2157</div>
2158<a id="a1a5bb4ab6841dd39e48089413cf8fe05"></a>
2159<h2 class="memtitle"><span class="permalink"><a href="#a1a5bb4ab6841dd39e48089413cf8fe05">&#9670;&nbsp;</a></span>Concat4dDim1Test()</h2>
2160
2161<div class="memitem">
2162<div class="memproto">
2163 <table class="memname">
2164 <tr>
2165 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDim1Test </td>
2166 <td>(</td>
2167 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2168 <td class="paramname"><em>workloadFactory</em>, </td>
2169 </tr>
2170 <tr>
2171 <td class="paramkey"></td>
2172 <td></td>
2173 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2174 <td class="paramname"><em>memoryManager</em>&#160;</td>
2175 </tr>
2176 <tr>
2177 <td></td>
2178 <td>)</td>
2179 <td></td><td></td>
2180 </tr>
2181 </table>
2182</div><div class="memdoc">
2183
2184<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02285">2285</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2185<div class="fragment"><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>&#160;{</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span>&#160; <span class="keywordflow">return</span> Concat4dDim1TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>&#160;}</div></div><!-- fragment -->
2186</div>
2187</div>
2188<a id="ac0a20ee6a32563959bbbbd16358d2a07"></a>
2189<h2 class="memtitle"><span class="permalink"><a href="#ac0a20ee6a32563959bbbbd16358d2a07">&#9670;&nbsp;</a></span>Concat4dDim1TestImpl()</h2>
2190
2191<div class="memitem">
2192<div class="memproto">
2193 <table class="memname">
2194 <tr>
2195 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDim1TestImpl </td>
2196 <td>(</td>
2197 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2198 <td class="paramname"><em>workloadFactory</em>, </td>
2199 </tr>
2200 <tr>
2201 <td class="paramkey"></td>
2202 <td></td>
2203 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2204 <td class="paramname"><em>memoryManager</em>, </td>
2205 </tr>
2206 <tr>
2207 <td class="paramkey"></td>
2208 <td></td>
2209 <td class="paramtype">float&#160;</td>
2210 <td class="paramname"><em>qScale</em>, </td>
2211 </tr>
2212 <tr>
2213 <td class="paramkey"></td>
2214 <td></td>
2215 <td class="paramtype">int32_t&#160;</td>
2216 <td class="paramname"><em>qOffset</em>&#160;</td>
2217 </tr>
2218 <tr>
2219 <td></td>
2220 <td>)</td>
2221 <td></td><td></td>
2222 </tr>
2223 </table>
2224</div><div class="memdoc">
2225
2226<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01502">1502</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2227
2228<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
2229<div class="fragment"><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>&#160;{</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>&#160;</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result = Concat4dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 1, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>&#160;</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>&#160; {</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>&#160;</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>&#160;</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>&#160; 31.0f, 32.0f</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>&#160; },</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>&#160;</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2230<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
2231<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2232</div><!-- fragment -->
2233</div>
2234</div>
2235<a id="aa13bf446c9b813c55ce96b49e5a17154"></a>
2236<h2 class="memtitle"><span class="permalink"><a href="#aa13bf446c9b813c55ce96b49e5a17154">&#9670;&nbsp;</a></span>Concat4dDim1Uint8Test()</h2>
2237
2238<div class="memitem">
2239<div class="memproto">
2240 <table class="memname">
2241 <tr>
2242 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDim1Uint8Test </td>
2243 <td>(</td>
2244 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2245 <td class="paramname"><em>workloadFactory</em>, </td>
2246 </tr>
2247 <tr>
2248 <td class="paramkey"></td>
2249 <td></td>
2250 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2251 <td class="paramname"><em>memoryManager</em>&#160;</td>
2252 </tr>
2253 <tr>
2254 <td></td>
2255 <td>)</td>
2256 <td></td><td></td>
2257 </tr>
2258 </table>
2259</div><div class="memdoc">
2260
2261<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02861">2861</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2262<div class="fragment"><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>&#160;{</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>&#160; <span class="keywordflow">return</span> Concat4dDim1TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>&#160;}</div></div><!-- fragment -->
2263</div>
2264</div>
2265<a id="a1d148bdca4ed20301d41d73398dd90e5"></a>
2266<h2 class="memtitle"><span class="permalink"><a href="#a1d148bdca4ed20301d41d73398dd90e5">&#9670;&nbsp;</a></span>Concat4dDim2Test()</h2>
2267
2268<div class="memitem">
2269<div class="memproto">
2270 <table class="memname">
2271 <tr>
2272 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDim2Test </td>
2273 <td>(</td>
2274 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2275 <td class="paramname"><em>workloadFactory</em>, </td>
2276 </tr>
2277 <tr>
2278 <td class="paramkey"></td>
2279 <td></td>
2280 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2281 <td class="paramname"><em>memoryManager</em>&#160;</td>
2282 </tr>
2283 <tr>
2284 <td></td>
2285 <td>)</td>
2286 <td></td><td></td>
2287 </tr>
2288 </table>
2289</div><div class="memdoc">
2290
2291<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02292">2292</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2292<div class="fragment"><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>&#160;{</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>&#160; <span class="keywordflow">return</span> Concat4dDim2TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>&#160;}</div></div><!-- fragment -->
2293</div>
2294</div>
2295<a id="ad14affe1f35650404637e949e6cda6d7"></a>
2296<h2 class="memtitle"><span class="permalink"><a href="#ad14affe1f35650404637e949e6cda6d7">&#9670;&nbsp;</a></span>Concat4dDim2TestImpl()</h2>
2297
2298<div class="memitem">
2299<div class="memproto">
2300 <table class="memname">
2301 <tr>
2302 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDim2TestImpl </td>
2303 <td>(</td>
2304 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2305 <td class="paramname"><em>workloadFactory</em>, </td>
2306 </tr>
2307 <tr>
2308 <td class="paramkey"></td>
2309 <td></td>
2310 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2311 <td class="paramname"><em>memoryManager</em>, </td>
2312 </tr>
2313 <tr>
2314 <td class="paramkey"></td>
2315 <td></td>
2316 <td class="paramtype">float&#160;</td>
2317 <td class="paramname"><em>qScale</em>, </td>
2318 </tr>
2319 <tr>
2320 <td class="paramkey"></td>
2321 <td></td>
2322 <td class="paramtype">int32_t&#160;</td>
2323 <td class="paramname"><em>qOffset</em>&#160;</td>
2324 </tr>
2325 <tr>
2326 <td></td>
2327 <td>)</td>
2328 <td></td><td></td>
2329 </tr>
2330 </table>
2331</div><div class="memdoc">
2332
2333<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01542">1542</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2334
2335<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
2336<div class="fragment"><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>&#160;{</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>&#160;</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result = Concat4dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 2, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>&#160;</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>&#160; {</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>&#160;</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>&#160;</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>&#160; 31.0f, 32.0f</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>&#160; },</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>&#160;</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2337<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
2338<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2339</div><!-- fragment -->
2340</div>
2341</div>
2342<a id="a9a1400c7948e6536489676848c40630f"></a>
2343<h2 class="memtitle"><span class="permalink"><a href="#a9a1400c7948e6536489676848c40630f">&#9670;&nbsp;</a></span>Concat4dDim2Uint8Test()</h2>
2344
2345<div class="memitem">
2346<div class="memproto">
2347 <table class="memname">
2348 <tr>
2349 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDim2Uint8Test </td>
2350 <td>(</td>
2351 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2352 <td class="paramname"><em>workloadFactory</em>, </td>
2353 </tr>
2354 <tr>
2355 <td class="paramkey"></td>
2356 <td></td>
2357 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2358 <td class="paramname"><em>memoryManager</em>&#160;</td>
2359 </tr>
2360 <tr>
2361 <td></td>
2362 <td>)</td>
2363 <td></td><td></td>
2364 </tr>
2365 </table>
2366</div><div class="memdoc">
2367
2368<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02868">2868</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2369<div class="fragment"><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>&#160;{</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>&#160; <span class="keywordflow">return</span> Concat4dDim2TestImpl&lt;DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.5f, -1);</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>&#160;}</div></div><!-- fragment -->
2370</div>
2371</div>
2372<a id="a2081650a5142448a5db4065819da2089"></a>
2373<h2 class="memtitle"><span class="permalink"><a href="#a2081650a5142448a5db4065819da2089">&#9670;&nbsp;</a></span>Concat4dDim3Test()</h2>
2374
2375<div class="memitem">
2376<div class="memproto">
2377 <table class="memname">
2378 <tr>
2379 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Concat4dDim3Test </td>
2380 <td>(</td>
2381 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2382 <td class="paramname"><em>workloadFactory</em>, </td>
2383 </tr>
2384 <tr>
2385 <td class="paramkey"></td>
2386 <td></td>
2387 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2388 <td class="paramname"><em>memoryManager</em>, </td>
2389 </tr>
2390 <tr>
2391 <td class="paramkey"></td>
2392 <td></td>
2393 <td class="paramtype">bool&#160;</td>
2394 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2395 </tr>
2396 <tr>
2397 <td></td>
2398 <td>)</td>
2399 <td></td><td></td>
2400 </tr>
2401 </table>
2402</div><div class="memdoc">
2403
2404<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02299">2299</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2405<div class="fragment"><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>&#160;{</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>&#160; <span class="keywordflow">return</span> Concat4dDim3TestImpl&lt;DataType::Float32&gt;(workloadFactory, memoryManager, 0.0f, 0, useSubtensor);</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>&#160;}</div></div><!-- fragment -->
2406</div>
2407</div>
2408<a id="a5d8473a59cf76ad1914b36fd8d45f00b"></a>
2409<h2 class="memtitle"><span class="permalink"><a href="#a5d8473a59cf76ad1914b36fd8d45f00b">&#9670;&nbsp;</a></span>Concat4dDim3TestImpl()</h2>
2410
2411<div class="memitem">
2412<div class="memproto">
2413 <table class="memname">
2414 <tr>
2415 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dDim3TestImpl </td>
2416 <td>(</td>
2417 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2418 <td class="paramname"><em>workloadFactory</em>, </td>
2419 </tr>
2420 <tr>
2421 <td class="paramkey"></td>
2422 <td></td>
2423 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2424 <td class="paramname"><em>memoryManager</em>, </td>
2425 </tr>
2426 <tr>
2427 <td class="paramkey"></td>
2428 <td></td>
2429 <td class="paramtype">float&#160;</td>
2430 <td class="paramname"><em>qScale</em>, </td>
2431 </tr>
2432 <tr>
2433 <td class="paramkey"></td>
2434 <td></td>
2435 <td class="paramtype">int32_t&#160;</td>
2436 <td class="paramname"><em>qOffset</em>, </td>
2437 </tr>
2438 <tr>
2439 <td class="paramkey"></td>
2440 <td></td>
2441 <td class="paramtype">bool&#160;</td>
2442 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2443 </tr>
2444 <tr>
2445 <td></td>
2446 <td>)</td>
2447 <td></td><td></td>
2448 </tr>
2449 </table>
2450</div><div class="memdoc">
2451
2452<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01582">1582</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2453
2454<p class="reference">References <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
2455<div class="fragment"><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>&#160;{</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>&#160;</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result = Concat4dTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>&#160; workloadFactory, memoryManager, outputTensorInfo, 3, useSubtensor, qScale, qOffset);</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>&#160;</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>&#160; result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 4&gt;(outputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>&#160; {</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>&#160;</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>&#160;</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>&#160; 31.0f, 32.0f</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>&#160; },</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>&#160;</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2456<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
2457<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2458</div><!-- fragment -->
2459</div>
2460</div>
2461<a id="a3de096f0e07787adaf34b6d348ca9543"></a>
2462<h2 class="memtitle"><span class="permalink"><a href="#a3de096f0e07787adaf34b6d348ca9543">&#9670;&nbsp;</a></span>Concat4dDim3Uint8Test()</h2>
2463
2464<div class="memitem">
2465<div class="memproto">
2466 <table class="memname">
2467 <tr>
2468 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Concat4dDim3Uint8Test </td>
2469 <td>(</td>
2470 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2471 <td class="paramname"><em>workloadFactory</em>, </td>
2472 </tr>
2473 <tr>
2474 <td class="paramkey"></td>
2475 <td></td>
2476 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2477 <td class="paramname"><em>memoryManager</em>, </td>
2478 </tr>
2479 <tr>
2480 <td class="paramkey"></td>
2481 <td></td>
2482 <td class="paramtype">bool&#160;</td>
2483 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2484 </tr>
2485 <tr>
2486 <td></td>
2487 <td>)</td>
2488 <td></td><td></td>
2489 </tr>
2490 </table>
2491</div><div class="memdoc">
2492
2493<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02875">2875</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2494<div class="fragment"><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>&#160;{</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>&#160; <span class="keywordflow">return</span> Concat4dDim3TestImpl&lt;DataType::QAsymmU8&gt;(</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>&#160; workloadFactory, memoryManager, 0.5f, -1, useSubtensor);</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span>&#160;}</div></div><!-- fragment -->
2495</div>
2496</div>
2497<a id="aeef13eb0a86ade1b1c92357c44ed8add"></a>
2498<h2 class="memtitle"><span class="permalink"><a href="#aeef13eb0a86ade1b1c92357c44ed8add">&#9670;&nbsp;</a></span>Concat4dTestImpl()</h2>
2499
2500<div class="memitem">
2501<div class="memproto">
2502 <table class="memname">
2503 <tr>
2504 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Concat4dTestImpl </td>
2505 <td>(</td>
2506 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2507 <td class="paramname"><em>workloadFactory</em>, </td>
2508 </tr>
2509 <tr>
2510 <td class="paramkey"></td>
2511 <td></td>
2512 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2513 <td class="paramname"><em>memoryManager</em>, </td>
2514 </tr>
2515 <tr>
2516 <td class="paramkey"></td>
2517 <td></td>
2518 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;&#160;</td>
2519 <td class="paramname"><em>outputTensorInfo</em>, </td>
2520 </tr>
2521 <tr>
2522 <td class="paramkey"></td>
2523 <td></td>
2524 <td class="paramtype">unsigned int&#160;</td>
2525 <td class="paramname"><em>dimension</em>, </td>
2526 </tr>
2527 <tr>
2528 <td class="paramkey"></td>
2529 <td></td>
2530 <td class="paramtype">bool&#160;</td>
2531 <td class="paramname"><em>useSubtensor</em>, </td>
2532 </tr>
2533 <tr>
2534 <td class="paramkey"></td>
2535 <td></td>
2536 <td class="paramtype">float&#160;</td>
2537 <td class="paramname"><em>qScale</em>, </td>
2538 </tr>
2539 <tr>
2540 <td class="paramkey"></td>
2541 <td></td>
2542 <td class="paramtype">int32_t&#160;</td>
2543 <td class="paramname"><em>qOffset</em>&#160;</td>
2544 </tr>
2545 <tr>
2546 <td></td>
2547 <td>)</td>
2548 <td></td><td></td>
2549 </tr>
2550 </table>
2551</div><div class="memdoc">
2552
2553<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01399">1399</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2554
2555<p class="reference">References <a class="el" href="_tensor_8hpp_source.xhtml#l00093">TensorInfo::GetNumElements()</a>, and <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>.</p>
2556<div class="fragment"><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>&#160;{</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>&#160;</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>&#160; <span class="keyword">auto</span> input0 = MakeTensor&lt;T, 4&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>&#160; {</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>&#160; 1.0f, 2.0f,</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>&#160; 3.0f, 4.0f,</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>&#160; 5.0f, 6.0f,</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>&#160; 7.0f, 8.0f,</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>&#160; 9.0f, 10.0f,</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>&#160; 11.0f, 12.0f</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>&#160; },</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>&#160;</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 4&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>&#160; {</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>&#160; 11.0f, 12.0f,</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>&#160; 13.0f, 14.0f,</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>&#160; 15.0f, 16.0f,</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>&#160; 17.0f, 18.0f,</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>&#160; 19.0f, 20.0f,</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>&#160; 21.0f, 22.0f</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>&#160; },</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>&#160;</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 4&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>&#160; {</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>&#160; 21.0f, 22.0f,</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>&#160; 23.0f, 24.0f,</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>&#160; 25.0f, 26.0f,</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>&#160; 27.0f, 28.0f,</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>&#160; 29.0f, 30.0f,</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>&#160; 31.0f, 32.0f</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>&#160; },</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>&#160; qScale, qOffset));</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>&#160;</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>&#160;</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>&#160; output.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>&#160;</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>&#160; Concatenate&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>&#160; memoryManager,</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>&#160; {inputTensorInfo, inputTensorInfo, inputTensorInfo},</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>&#160; {input0.data(), input1.data(), input2.data()},</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>&#160; output.data(),</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>&#160; dimension,</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>&#160; useSubtensor);</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>&#160;</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>&#160; result.output = MakeTensor&lt;T, 4&gt;(outputTensorInfo, output);</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2557<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2558<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00093">Tensor.hpp:93</a></div></div>
2559</div><!-- fragment -->
2560</div>
2561</div>
2562<a id="a9f799a2fd4acd720585f5a42249e0371"></a>
2563<h2 class="memtitle"><span class="permalink"><a href="#a9f799a2fd4acd720585f5a42249e0371">&#9670;&nbsp;</a></span>ConcatBFloat16Test()</h2>
2564
2565<div class="memitem">
2566<div class="memproto">
2567 <table class="memname">
2568 <tr>
2569 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="classarmnn_1_1_b_float16.xhtml">BFloat16</a>, 3&gt; ConcatBFloat16Test </td>
2570 <td>(</td>
2571 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2572 <td class="paramname"><em>workloadFactory</em>, </td>
2573 </tr>
2574 <tr>
2575 <td class="paramkey"></td>
2576 <td></td>
2577 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2578 <td class="paramname"><em>memoryManager</em>&#160;</td>
2579 </tr>
2580 <tr>
2581 <td></td>
2582 <td>)</td>
2583 <td></td><td></td>
2584 </tr>
2585 </table>
2586</div><div class="memdoc">
2587
2588<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02345">2345</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2589<div class="fragment"><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span>&#160;{</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>&#160; <span class="keywordflow">return</span> Concat3dDim1TestImpl&lt;DataType::BFloat16&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>&#160;}</div></div><!-- fragment -->
2590</div>
2591</div>
2592<a id="a9d679b4a18c9cadc563bd77a726a3726"></a>
2593<h2 class="memtitle"><span class="permalink"><a href="#a9d679b4a18c9cadc563bd77a726a3726">&#9670;&nbsp;</a></span>ConcatDifferentInputOutputQParamTest()</h2>
2594
2595<div class="memitem">
2596<div class="memproto">
2597 <table class="memname">
2598 <tr>
2599 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 3&gt; ConcatDifferentInputOutputQParamTest </td>
2600 <td>(</td>
2601 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2602 <td class="paramname"><em>workloadFactory</em>, </td>
2603 </tr>
2604 <tr>
2605 <td class="paramkey"></td>
2606 <td></td>
2607 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2608 <td class="paramname"><em>memoryManager</em>, </td>
2609 </tr>
2610 <tr>
2611 <td class="paramkey"></td>
2612 <td></td>
2613 <td class="paramtype">bool&#160;</td>
2614 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2615 </tr>
2616 <tr>
2617 <td></td>
2618 <td>)</td>
2619 <td></td><td></td>
2620 </tr>
2621 </table>
2622</div><div class="memdoc">
2623
2624<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l01916">1916</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2625
2626<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01135">IWorkloadFactory::CreateConcat()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00242">armnn::CreateDescriptorForConcatenation()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">IWorkloadFactory::CreateSubTensorHandle()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00115">ConcatQueueDescriptor::m_ViewOrigins</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>, and <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">IWorkloadFactory::SupportsSubTensors()</a>.</p>
2627<div class="fragment"><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160;{</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160;</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>&#160; <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 3, 6, 3 }, ArmnnType);</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 3, 6, 2 }, ArmnnType);</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ 3, 6, 1 }, ArmnnType);</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>&#160;</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>&#160; std::vector&lt;TensorShape&gt; inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()});</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160;</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>&#160; <span class="comment">// Quantized input1 tensor.</span></div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale1 = 0.5f;</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160; <span class="keyword">const</span> int32_t inputOffset1 = 5;</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>&#160;</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 3&gt;(inputTensorInfo1, std::vector&lt;T&gt;(</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160; {</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160; 1, 2, 3,</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160; 4, 5, 6,</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160; 7, 8, 9,</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160; 10, 11, 12,</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160; 13, 14, 15,</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160; 16, 17, 18,</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160;</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160; 19, 20, 21,</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160; 22, 23, 24,</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160; 25, 26, 27,</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160; 28, 29, 30,</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160; 31, 32, 33,</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160; 34, 35, 36</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160; }));</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>&#160;</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160; <span class="comment">// Quatized input2 tensor.</span></div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale2 = 0.2f;</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160; <span class="keyword">const</span> int32_t inputOffset2 = 10;</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 3&gt;(inputTensorInfo2, std::vector&lt;T&gt;(</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160; {</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160; 37, 38, 39,</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160; 40, 41, 42,</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160; 43, 44, 45,</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160; 46, 47, 48,</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160; 49, 50, 51,</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160; 52, 53, 54</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160; }));</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160;</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160; <span class="comment">// Quantized output tensor.</span></div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> outputScale = 0.1f;</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160; <span class="keyword">const</span> int32_t outputOffset = 20;</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>&#160;</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160;</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160; ret.outputExpected = MakeTensor&lt;T, 3&gt;(outputTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160; {</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160; 0, 5, 74,</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160; 10, 15, 76,</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160; 20, 25, 78,</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160; 30, 35, 80,</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160; 40, 45, 82,</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160; 50, 55, 84,</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160;</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160; 60, 65, 86,</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160; 70, 75, 88,</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160; 80, 85, 90,</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160; 90, 95, 92,</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160; 100, 105, 94,</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160; 110, 115, 96,</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160; 120, 125, 98,</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160; 130, 135, 100,</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160; 140, 145, 102,</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160; 150, 155, 104,</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160; 160, 165, 106,</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160; 170, 175, 108</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160; }));</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160;</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160; outputTensorInfo.SetQuantizationScale(outputScale);</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160; outputTensorInfo.SetQuantizationOffset(outputOffset);</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160; inputTensorInfo1.SetQuantizationScale(inputScale1);</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160; inputTensorInfo1.SetQuantizationOffset(inputOffset1);</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160; inputTensorInfo2.SetQuantizationScale(inputScale2);</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160; inputTensorInfo2.SetQuantizationOffset(inputOffset2);</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>&#160;</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160; std::vector&lt;unsigned int&gt; wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160; std::vector&lt;unsigned int&gt; wOrigin2 = { 0, 0, 2 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160;</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160; <span class="keywordtype">bool</span> subTensorsSupported = useSubtensor &amp;&amp; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle1 =</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle2 =</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160; <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> desc = <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">CreateDescriptorForConcatenation</a>(</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160; inputTensorShapes.begin(),inputTensorShapes.end(), 2);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = desc;</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160; AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160;</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>&#160;</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160; inputHandle1-&gt;Allocate();</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160; inputHandle2-&gt;Allocate();</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160;</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &amp;input1[0][0][0]);</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &amp;input2[0][0][0]);</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>&#160;</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>&#160;</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0], outputHandle.get());</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>&#160;</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00104">WorkloadData.hpp:104</a></div></div>
2628<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2629<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ac043991b839903b2ba9da884e4020848"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">armnn::IWorkloadFactory::CreateSubTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateSubTensorHandle(ITensorHandle &amp;parent, TensorShape const &amp;subTensorShape, unsigned int const *subTensorOrigin) const =0</div></div>
2630<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
2631<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div>
2632<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00102">WorkloadData.hpp:102</a></div></div>
2633<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a32bb8d6cf5fc028bf501252767c08b21"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">armnn::IWorkloadFactory::CreateConcat</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConcat(const ConcatQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01135">WorkloadFactory.cpp:1135</a></div></div>
2634<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00115">WorkloadData.hpp:115</a></div></div>
2635<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00147">Descriptors.hpp:147</a></div></div>
2636<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
2637<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
2638<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
2639<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
2640<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2641<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00242">Descriptors.hpp:242</a></div></div>
2642<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
2643<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a37f4eba7877deb34f4d8d64c9bcb9ab5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">armnn::IWorkloadFactory::SupportsSubTensors</a></div><div class="ttdeci">virtual bool SupportsSubTensors() const =0</div></div>
2644</div><!-- fragment -->
2645</div>
2646</div>
2647<a id="ac4a1dff653419576cd96b81cf10b984e"></a>
2648<h2 class="memtitle"><span class="permalink"><a href="#ac4a1dff653419576cd96b81cf10b984e">&#9670;&nbsp;</a></span>ConcatDifferentInputOutputQParamTest< DataType::QAsymmU8 >()</h2>
2649
2650<div class="memitem">
2651<div class="memproto">
2652 <table class="memname">
2653 <tr>
2654 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType</a>&lt;DataType::QAsymmU8&gt;, 3&gt; <a class="el" href="_concat_test_impl_8hpp.xhtml#a6e1f3186d22d87b9fd8cd165fc93dd8b">ConcatDifferentInputOutputQParamTest</a>&lt; DataType::QAsymmU8 &gt; </td>
2655 <td>(</td>
2656 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2657 <td class="paramname"><em>workloadFactory</em>, </td>
2658 </tr>
2659 <tr>
2660 <td class="paramkey"></td>
2661 <td></td>
2662 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2663 <td class="paramname"><em>memoryManager</em>, </td>
2664 </tr>
2665 <tr>
2666 <td class="paramkey"></td>
2667 <td></td>
2668 <td class="paramtype">bool&#160;</td>
2669 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2670 </tr>
2671 <tr>
2672 <td></td>
2673 <td>)</td>
2674 <td></td><td></td>
2675 </tr>
2676 </table>
2677</div><div class="memdoc">
2678
2679</div>
2680</div>
2681<a id="a878b6bd50169d509d8ee47d79e3c87d0"></a>
2682<h2 class="memtitle"><span class="permalink"><a href="#a878b6bd50169d509d8ee47d79e3c87d0">&#9670;&nbsp;</a></span>ConcatDifferentInputOutputQParamTest< DataType::QSymmS16 >()</h2>
2683
2684<div class="memitem">
2685<div class="memproto">
2686 <table class="memname">
2687 <tr>
2688 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType</a>&lt;DataType::QSymmS16&gt;, 3&gt; <a class="el" href="_concat_test_impl_8hpp.xhtml#a6e1f3186d22d87b9fd8cd165fc93dd8b">ConcatDifferentInputOutputQParamTest</a>&lt; DataType::QSymmS16 &gt; </td>
2689 <td>(</td>
2690 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2691 <td class="paramname"><em>workloadFactory</em>, </td>
2692 </tr>
2693 <tr>
2694 <td class="paramkey"></td>
2695 <td></td>
2696 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2697 <td class="paramname"><em>memoryManager</em>, </td>
2698 </tr>
2699 <tr>
2700 <td class="paramkey"></td>
2701 <td></td>
2702 <td class="paramtype">bool&#160;</td>
2703 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2704 </tr>
2705 <tr>
2706 <td></td>
2707 <td>)</td>
2708 <td></td><td></td>
2709 </tr>
2710 </table>
2711</div><div class="memdoc">
2712
2713</div>
2714</div>
2715<a id="a3a7534d69e8cc11c52b0a056ca82bcb8"></a>
2716<h2 class="memtitle"><span class="permalink"><a href="#a3a7534d69e8cc11c52b0a056ca82bcb8">&#9670;&nbsp;</a></span>Concatenate()</h2>
2717
2718<div class="memitem">
2719<div class="memproto">
2720 <table class="memname">
2721 <tr>
2722 <td class="memname">void Concatenate </td>
2723 <td>(</td>
2724 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2725 <td class="paramname"><em>workloadFactory</em>, </td>
2726 </tr>
2727 <tr>
2728 <td class="paramkey"></td>
2729 <td></td>
2730 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2731 <td class="paramname"><em>memoryManager</em>, </td>
2732 </tr>
2733 <tr>
2734 <td class="paramkey"></td>
2735 <td></td>
2736 <td class="paramtype">std::initializer_list&lt; const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt;&#160;</td>
2737 <td class="paramname"><em>inputTensorInfosOrig</em>, </td>
2738 </tr>
2739 <tr>
2740 <td class="paramkey"></td>
2741 <td></td>
2742 <td class="paramtype">std::initializer_list&lt; T *&gt;&#160;</td>
2743 <td class="paramname"><em>inputsOrig</em>, </td>
2744 </tr>
2745 <tr>
2746 <td class="paramkey"></td>
2747 <td></td>
2748 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;&#160;</td>
2749 <td class="paramname"><em>outputTensorInfoOrig</em>, </td>
2750 </tr>
2751 <tr>
2752 <td class="paramkey"></td>
2753 <td></td>
2754 <td class="paramtype">T *&#160;</td>
2755 <td class="paramname"><em>output</em>, </td>
2756 </tr>
2757 <tr>
2758 <td class="paramkey"></td>
2759 <td></td>
2760 <td class="paramtype">unsigned int&#160;</td>
2761 <td class="paramname"><em>concatDim</em>, </td>
2762 </tr>
2763 <tr>
2764 <td class="paramkey"></td>
2765 <td></td>
2766 <td class="paramtype">bool&#160;</td>
2767 <td class="paramname"><em>useSubtensor</em>&#160;</td>
2768 </tr>
2769 <tr>
2770 <td></td>
2771 <td>)</td>
2772 <td></td><td></td>
2773 </tr>
2774 </table>
2775</div><div class="memdoc">
2776
2777<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00272">272</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2778
2779<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01135">IWorkloadFactory::CreateConcat()</a>, <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00026">CreateDescriptorForConcat()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">IWorkloadFactory::CreateSubTensorHandle()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_descriptors_8cpp_source.xhtml#l00184">OriginsDescriptor::GetNumDimensions()</a>, <a class="el" href="_descriptors_8cpp_source.xhtml#l00179">OriginsDescriptor::GetNumViews()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_descriptors_8cpp_source.xhtml#l00189">OriginsDescriptor::GetViewOrigin()</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00115">ConcatQueueDescriptor::m_ViewOrigins</a>, <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00046">NeedPermuteForConcat()</a>, and <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">IWorkloadFactory::SupportsSubTensors()</a>.</p>
2780<div class="fragment"><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160;{</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; BOOST_ASSERT_MSG(output != <span class="keyword">nullptr</span>, <span class="stringliteral">&quot;output must not be null&quot;</span>);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <span class="keywordflow">if</span> (output == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; {</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; <span class="comment">// Nullptr is an error in the test. By returning without doing the permutation</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; <span class="comment">// I expect the caller to fail the test. It still makes sense to report this as</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; <span class="comment">// an assert for Debug builds.</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; <span class="keywordflow">return</span>;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; }</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; <span class="comment">// Saves a copy of the parameters which we might need to change.</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; std::vector&lt;TensorInfo&gt; inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end());</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; std::vector&lt;T *&gt; inputs = inputsOrig;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = outputTensorInfoOrig;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permuteVector{0, 1, 2};</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; <span class="comment">// Holds and automatically releases memory for the reshaped input data.</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; std::vector&lt;std::vector&lt;T&gt;&gt; tmpInputDataStorage;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> inputCount = inputTensorInfos.size();</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160;</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; <span class="keywordtype">bool</span> needPermuteForConcat = <a class="code" href="_concat_test_impl_8cpp.xhtml#a905e011ae8536bbd643dd09495524596">NeedPermuteForConcat</a>(inputTensorInfos, concatDim);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160;</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; <span class="keywordflow">if</span> (needPermuteForConcat)</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; {</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; <span class="comment">// We need to permute the inputs, because concatenation along</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; <span class="comment">// the requested axis is not supported.</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; PermuteInputsForConcat&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; memoryManager,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; inputTensorInfos,</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; inputs,</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; tmpInputDataStorage,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; permuteVector,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; concatDim,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; outputTensorInfo);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; }</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; std::vector&lt;std::unique_ptr&lt;ITensorHandle&gt;&gt; inputHandles;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; inputHandles.reserve(inputCount);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> viewsDescriptor = <a class="code" href="_concat_test_impl_8cpp.xhtml#a908c80ff86d48fe1bc7cd4d4b1d00147">CreateDescriptorForConcat</a>(inputTensorInfos, concatDim);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = viewsDescriptor;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; <span class="keywordflow">if</span> (useSubtensor)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; queueDescriptor.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.reserve(viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a35546e7b56e6e972a495b48748478ede">GetNumViews</a>());</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a35546e7b56e6e972a495b48748478ede">GetNumViews</a>(); ++i)</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; {</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; queueDescriptor.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.emplace_back(std::vector&lt;unsigned int&gt;(viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">GetViewOrigin</a>(i),</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">GetViewOrigin</a>(i) + viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">GetNumDimensions</a>()));</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; }</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; inputCount; ++i)</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; {</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; inputTensorInfo = inputTensorInfos[i];</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle =</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; queueDescriptor.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[i].m_Origin.data()) :</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; inputHandles.emplace_back(std::move(inputHandle));</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; }</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160;</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160; }</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; {</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; inputCount; ++i)</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; {</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfos[i]);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; inputHandles.emplace_back(std::move(inputHandle));</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; }</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; }</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; inputCount; ++i)</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; {</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].<span class="keyword">get</span>());</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; }</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(queueDescriptor, workloadInfo);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; inputHandle : inputHandles)</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; {</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160;</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160;</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nextInputId = 0;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; inputHandle : inputHandles)</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; {</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputs[nextInputId]);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; ++nextInputId;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; }</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160;</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; <span class="keywordflow">if</span> (needPermuteForConcat)</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; {</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; PermuteOutputForConcat&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; memoryManager,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; permuteVector,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; std::move(outputHandle),</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; output);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; }</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; {</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(output, outputHandle.get());</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; }</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div>
2781<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2782<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ac043991b839903b2ba9da884e4020848"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">armnn::IWorkloadFactory::CreateSubTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateSubTensorHandle(ITensorHandle &amp;parent, TensorShape const &amp;subTensorShape, unsigned int const *subTensorOrigin) const =0</div></div>
2783<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div>
2784<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00102">WorkloadData.hpp:102</a></div></div>
2785<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a32bb8d6cf5fc028bf501252767c08b21"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">armnn::IWorkloadFactory::CreateConcat</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConcat(const ConcatQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01135">WorkloadFactory.cpp:1135</a></div></div>
2786<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_ab78e6fe963508c1ac5c00d04bb3361a3"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">armnn::OriginsDescriptor::GetViewOrigin</a></div><div class="ttdeci">const uint32_t * GetViewOrigin(uint32_t idx) const</div><div class="ttdoc">Return the view origin at the int value idx. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00189">Descriptors.cpp:189</a></div></div>
2787<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00115">WorkloadData.hpp:115</a></div></div>
2788<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00147">Descriptors.hpp:147</a></div></div>
2789<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a908c80ff86d48fe1bc7cd4d4b1d00147"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a908c80ff86d48fe1bc7cd4d4b1d00147">CreateDescriptorForConcat</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcat(const std::vector&lt; TensorInfo &gt; &amp;inputTensorInfos, unsigned int concatDim)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00026">ConcatTestImpl.cpp:26</a></div></div>
2790<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
2791<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00173">Types.hpp:173</a></div></div>
2792<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
2793<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a78e8266be865fdd92cadd04d6e25ae1f"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">armnn::OriginsDescriptor::GetNumDimensions</a></div><div class="ttdeci">uint32_t GetNumDimensions() const</div><div class="ttdoc">Get the number of dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00184">Descriptors.cpp:184</a></div></div>
2794<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
2795<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a35546e7b56e6e972a495b48748478ede"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a35546e7b56e6e972a495b48748478ede">armnn::OriginsDescriptor::GetNumViews</a></div><div class="ttdeci">uint32_t GetNumViews() const</div><div class="ttdoc">Get the number of views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00179">Descriptors.cpp:179</a></div></div>
2796<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a905e011ae8536bbd643dd09495524596"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a905e011ae8536bbd643dd09495524596">NeedPermuteForConcat</a></div><div class="ttdeci">bool NeedPermuteForConcat(const std::vector&lt; TensorInfo &gt; &amp;inputTensorInfos, unsigned int concatDim)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00046">ConcatTestImpl.cpp:46</a></div></div>
2797<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
2798<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a37f4eba7877deb34f4d8d64c9bcb9ab5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">armnn::IWorkloadFactory::SupportsSubTensors</a></div><div class="ttdeci">virtual bool SupportsSubTensors() const =0</div></div>
2799</div><!-- fragment -->
2800</div>
2801</div>
2802<a id="ac6e55fbcc8ae3dfa8c1762d343264006"></a>
2803<h2 class="memtitle"><span class="permalink"><a href="#ac6e55fbcc8ae3dfa8c1762d343264006">&#9670;&nbsp;</a></span>ConcatFloat16Test()</h2>
2804
2805<div class="memitem">
2806<div class="memproto">
2807 <table class="memname">
2808 <tr>
2809 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>, 3&gt; ConcatFloat16Test </td>
2810 <td>(</td>
2811 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2812 <td class="paramname"><em>workloadFactory</em>, </td>
2813 </tr>
2814 <tr>
2815 <td class="paramkey"></td>
2816 <td></td>
2817 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2818 <td class="paramname"><em>memoryManager</em>&#160;</td>
2819 </tr>
2820 <tr>
2821 <td></td>
2822 <td>)</td>
2823 <td></td><td></td>
2824 </tr>
2825 </table>
2826</div><div class="memdoc">
2827
2828<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02338">2338</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2829<div class="fragment"><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>&#160;{</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>&#160; <span class="keywordflow">return</span> Concat3dDim1TestImpl&lt;DataType::Float16&gt;(workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>&#160;}</div></div><!-- fragment -->
2830</div>
2831</div>
2832<a id="a4d293b286db068580f9d72048d4d7bfc"></a>
2833<h2 class="memtitle"><span class="permalink"><a href="#a4d293b286db068580f9d72048d4d7bfc">&#9670;&nbsp;</a></span>ConcatTest()</h2>
2834
2835<div class="memitem">
2836<div class="memproto">
2837 <table class="memname">
2838 <tr>
2839 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float,3&gt; ConcatTest </td>
2840 <td>(</td>
2841 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2842 <td class="paramname"><em>workloadFactory</em>, </td>
2843 </tr>
2844 <tr>
2845 <td class="paramkey"></td>
2846 <td></td>
2847 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2848 <td class="paramname"><em>memoryManager</em>&#160;</td>
2849 </tr>
2850 <tr>
2851 <td></td>
2852 <td>)</td>
2853 <td></td><td></td>
2854 </tr>
2855 </table>
2856</div><div class="memdoc">
2857
2858<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02072">2072</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2859
2860<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01135">IWorkloadFactory::CreateConcat()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">IWorkloadFactory::CreateSubTensorHandle()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00115">ConcatQueueDescriptor::m_ViewOrigins</a>, and <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">IWorkloadFactory::SupportsSubTensors()</a>.</p>
2861<div class="fragment"><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>&#160;{</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>&#160;</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>&#160;</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>&#160;</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>&#160;</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>&#160; <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::Float32);</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::Float32);</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::Float32);</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>&#160;</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,3&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>&#160;</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>&#160; ret.outputExpected = MakeTensor&lt;float, 3&gt;(outputTensorInfo, std::vector&lt;float&gt;(</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>&#160; {</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>&#160; 1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>&#160; 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>&#160; 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>&#160; 10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>&#160; 13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>&#160; 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>&#160;</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>&#160; 19.0f, 20.0f, 21.0f,</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>&#160; 22.0f, 23.0f, 24.0f,</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>&#160; 25.0f, 26.0f, 27.0f,</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>&#160; 28.0f, 29.0f, 30.0f,</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>&#160; 31.0f, 32.0f, 33.0f,</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>&#160; 34.0f, 35.0f, 36.0f,</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>&#160;</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>&#160; 37.0f, 38.0f, 39.0f,</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>&#160; 40.0f, 41.0f, 42.0f,</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>&#160; 43.0f, 44.0f, 45.0f,</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>&#160; 46.0f, 47.0f, 48.0f,</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>&#160; 49.0f, 50.0f, 51.0f,</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>&#160; 52.0f, 53.0f, 54.0f,</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>&#160; })</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>&#160; );</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>&#160;</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;float, 3&gt;(inputTensorInfo1, std::vector&lt;float&gt;(</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>&#160; {</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>&#160; 1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>&#160; 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>&#160; 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>&#160; 10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>&#160; 13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>&#160; 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>&#160;</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>&#160; 19.0f, 20.0f, 21.0f,</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>&#160; 22.0f, 23.0f, 24.0f,</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>&#160; 25.0f, 26.0f, 27.0f,</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>&#160; 28.0f, 29.0f, 30.0f,</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>&#160; 31.0f, 32.0f, 33.0f,</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>&#160; 34.0f, 35.0f, 36.0f,</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>&#160; })</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>&#160; );</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>&#160;</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;float, 3&gt;(inputTensorInfo2, std::vector&lt;float&gt;(</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>&#160; {</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>&#160; 37.0f, 38.0f, 39.0f,</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>&#160; 40.0f, 41.0f, 42.0f,</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>&#160; 43.0f, 44.0f, 45.0f,</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>&#160; 46.0f, 47.0f, 48.0f,</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>&#160; 49.0f, 50.0f, 51.0f,</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>&#160; 52.0f, 53.0f, 54.0f,</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>&#160; })</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>&#160; );</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>&#160;</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>&#160; std::vector&lt;unsigned int&gt; wOrigin1 = {0, 0, 0}; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>&#160;</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>&#160; std::vector&lt;unsigned int&gt; wOrigin2 = {2, 0, 0}; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>&#160;</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>&#160;</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>&#160; <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>&#160;</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle1 =</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>&#160;</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle2 =</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>&#160;</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>&#160; 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2862<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2863<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ac043991b839903b2ba9da884e4020848"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">armnn::IWorkloadFactory::CreateSubTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateSubTensorHandle(ITensorHandle &amp;parent, TensorShape const &amp;subTensorShape, unsigned int const *subTensorOrigin) const =0</div></div>
2864<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
2865<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00102">WorkloadData.hpp:102</a></div></div>
2866<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a32bb8d6cf5fc028bf501252767c08b21"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">armnn::IWorkloadFactory::CreateConcat</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConcat(const ConcatQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01135">WorkloadFactory.cpp:1135</a></div></div>
2867<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00115">WorkloadData.hpp:115</a></div></div>
2868<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
2869<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
2870<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
2871<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
2872<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2873<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
2874<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a37f4eba7877deb34f4d8d64c9bcb9ab5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">armnn::IWorkloadFactory::SupportsSubTensors</a></div><div class="ttdeci">virtual bool SupportsSubTensors() const =0</div></div>
2875</div><!-- fragment -->
2876</div>
2877</div>
2878<a id="a66df3b4ed5c8e464dcba94c2afc2b432"></a>
2879<h2 class="memtitle"><span class="permalink"><a href="#a66df3b4ed5c8e464dcba94c2afc2b432">&#9670;&nbsp;</a></span>ConcatUint16Test()</h2>
2880
2881<div class="memitem">
2882<div class="memproto">
2883 <table class="memname">
2884 <tr>
2885 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint16_t, 3&gt; ConcatUint16Test </td>
2886 <td>(</td>
2887 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2888 <td class="paramname"><em>workloadFactory</em>, </td>
2889 </tr>
2890 <tr>
2891 <td class="paramkey"></td>
2892 <td></td>
2893 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2894 <td class="paramname"><em>memoryManager</em>&#160;</td>
2895 </tr>
2896 <tr>
2897 <td></td>
2898 <td>)</td>
2899 <td></td><td></td>
2900 </tr>
2901 </table>
2902</div><div class="memdoc">
2903
2904<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02635">2635</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2905
2906<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01135">IWorkloadFactory::CreateConcat()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">IWorkloadFactory::CreateSubTensorHandle()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00115">ConcatQueueDescriptor::m_ViewOrigins</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::QSymmS16</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>, and <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">IWorkloadFactory::SupportsSubTensors()</a>.</p>
2907<div class="fragment"><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>&#160;{</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>&#160;</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>&#160;</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>&#160;</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>&#160;</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>&#160; <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QSymmS16);</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QSymmS16);</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QSymmS16);</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>&#160;</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>&#160; <span class="comment">// Arbitrary scale and offsets. They don&#39;t really matter as the Concat operator doesn&#39;t dequantize/quantize them.</span></div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> scale = 0.13497836f;</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>&#160; <span class="keyword">const</span> int32_t offset = -7;</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>&#160;</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(scale);</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>&#160; outputTensorInfo.SetQuantizationOffset(offset);</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>&#160; inputTensorInfo1.SetQuantizationScale(scale);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>&#160; inputTensorInfo1.SetQuantizationOffset(offset);</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>&#160; inputTensorInfo2.SetQuantizationScale(scale);</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span>&#160; inputTensorInfo2.SetQuantizationOffset(offset);</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>&#160;</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint16_t, 3&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>&#160;</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>&#160; ret.outputExpected = MakeTensor&lt;uint16_t, 3&gt;(outputTensorInfo, std::vector&lt;uint16_t&gt;(</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span>&#160; {</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>&#160; 7, 8, 9,</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>&#160; 10, 11, 12,</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>&#160; 13, 14, 15,</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>&#160; 16, 17, 18,</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>&#160;</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>&#160; 19, 20, 21,</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>&#160; 22, 23, 24,</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>&#160; 25, 26, 27,</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>&#160; 28, 29, 30,</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>&#160; 31, 32, 33,</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>&#160; 34, 35, 36,</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>&#160;</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>&#160; 37, 38, 39,</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>&#160; 40, 41, 42,</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>&#160; 43, 44, 45,</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>&#160; 46, 47, 48,</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>&#160; 49, 50, 51,</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>&#160; 52, 53, 54,</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>&#160; }));</div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>&#160;</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;uint16_t, 3&gt;(inputTensorInfo1, std::vector&lt;uint16_t&gt;(</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>&#160; {</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>&#160; 7, 8, 9,</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>&#160; 10, 11, 12,</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>&#160; 13, 14, 15,</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>&#160; 16, 17, 18,</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>&#160;</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>&#160; 19, 20, 21,</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span>&#160; 22, 23, 24,</div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>&#160; 25, 26, 27,</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>&#160; 28, 29, 30,</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>&#160; 31, 32, 33,</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>&#160; 34, 35, 36,</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>&#160; }));</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>&#160;</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;uint16_t, 3&gt;(inputTensorInfo2, std::vector&lt;uint16_t&gt;(</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>&#160; {</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>&#160; 37, 38, 39,</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>&#160; 40, 41, 42,</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>&#160; 43, 44, 45,</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>&#160; 46, 47, 48,</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>&#160; 49, 50, 51,</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>&#160; 52, 53, 54,</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>&#160; }));</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>&#160;</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>&#160; std::vector&lt;unsigned int&gt; wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>&#160;</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>&#160; std::vector&lt;unsigned int&gt; wOrigin2 = { 2, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>&#160;</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>&#160;</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>&#160;</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>&#160; <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>&#160;</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle1 =</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>&#160;</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle2 =</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>&#160;</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>&#160;</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>&#160; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>&#160; AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>&#160;</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>&#160;</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>&#160;</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>&#160; inputHandle1-&gt;Allocate();</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>&#160; inputHandle2-&gt;Allocate();</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>&#160;</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &amp;input1[0][0][0]);</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &amp;input2[0][0][0]);</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>&#160;</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>&#160;</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0], outputHandle.get());</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>&#160;</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00104">WorkloadData.hpp:104</a></div></div>
2908<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2909<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ac043991b839903b2ba9da884e4020848"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">armnn::IWorkloadFactory::CreateSubTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateSubTensorHandle(ITensorHandle &amp;parent, TensorShape const &amp;subTensorShape, unsigned int const *subTensorOrigin) const =0</div></div>
2910<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
2911<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00102">WorkloadData.hpp:102</a></div></div>
2912<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a32bb8d6cf5fc028bf501252767c08b21"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">armnn::IWorkloadFactory::CreateConcat</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConcat(const ConcatQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01135">WorkloadFactory.cpp:1135</a></div></div>
2913<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00115">WorkloadData.hpp:115</a></div></div>
2914<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00259">Tensor.cpp:259</a></div></div>
2915<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
2916<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
2917<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
2918<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
2919<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2920<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
2921<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a37f4eba7877deb34f4d8d64c9bcb9ab5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">armnn::IWorkloadFactory::SupportsSubTensors</a></div><div class="ttdeci">virtual bool SupportsSubTensors() const =0</div></div>
2922</div><!-- fragment -->
2923</div>
2924</div>
2925<a id="aa1491773368b57bfbe2a737a05c041fa"></a>
2926<h2 class="memtitle"><span class="permalink"><a href="#aa1491773368b57bfbe2a737a05c041fa">&#9670;&nbsp;</a></span>ConcatUint8DifferentQParamsTest()</h2>
2927
2928<div class="memitem">
2929<div class="memproto">
2930 <table class="memname">
2931 <tr>
2932 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; ConcatUint8DifferentQParamsTest </td>
2933 <td>(</td>
2934 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2935 <td class="paramname"><em>workloadFactory</em>, </td>
2936 </tr>
2937 <tr>
2938 <td class="paramkey"></td>
2939 <td></td>
2940 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2941 <td class="paramname"><em>memoryManager</em>&#160;</td>
2942 </tr>
2943 <tr>
2944 <td></td>
2945 <td>)</td>
2946 <td></td><td></td>
2947 </tr>
2948 </table>
2949</div><div class="memdoc">
2950
2951<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02352">2352</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2952
2953<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01135">IWorkloadFactory::CreateConcat()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">IWorkloadFactory::CreateSubTensorHandle()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00115">ConcatQueueDescriptor::m_ViewOrigins</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>, and <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">IWorkloadFactory::SupportsSubTensors()</a>.</p>
2954<div class="fragment"><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>&#160;{</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>&#160;</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>&#160;</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>&#160;</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>&#160;</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>&#160; <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QAsymmU8);</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QAsymmU8);</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QAsymmU8);</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>&#160;</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span>&#160; <span class="comment">// Quantized input1 tensor. Range [-3, 1]</span></div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale1 = 0.015686f;</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>&#160; <span class="keyword">const</span> int32_t inputOffset1 = 192;</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>&#160;</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;uint8_t, 3&gt;(inputTensorInfo1, std::vector&lt;uint8_t&gt;(</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>&#160; {</div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>&#160; 7, 8, 9,</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>&#160; 10, 11, 12,</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>&#160; 13, 14, 15,</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>&#160; 16, 17, 18,</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>&#160;</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>&#160; 19, 20, 21,</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>&#160; 22, 23, 24,</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>&#160; 25, 26, 27,</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>&#160; 28, 29, 30,</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>&#160; 31, 32, 33,</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>&#160; 34, 35, 36,</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>&#160; })</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>&#160; );</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>&#160;</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>&#160; <span class="comment">// Quatized input2 tensor. Range [-1, 4]</span></div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale2 = 0.019608f;</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>&#160; <span class="keyword">const</span> int32_t inputOffset2 = 50;</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>&#160;</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;uint8_t, 3&gt;(inputTensorInfo2, std::vector&lt;uint8_t&gt;(</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>&#160; {</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>&#160; 37, 38, 39,</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>&#160; 40, 41, 42,</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>&#160; 43, 44, 45,</div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>&#160; 46, 47, 48,</div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span>&#160; 49, 50, 51,</div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>&#160; 52, 53, 54,</div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>&#160; })</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>&#160; );</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>&#160;</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>&#160; <span class="comment">// Output has the same quantization parameters than input1,</span></div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>&#160; <span class="comment">// so that only the requantization of input2 is required</span></div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> outputScale = 0.015686f;</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>&#160; <span class="keyword">const</span> int32_t outputOffset = 192;</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>&#160;</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 3&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>&#160;</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>&#160; ret.outputExpected = MakeTensor&lt;uint8_t, 3&gt;(outputTensorInfo, std::vector&lt;uint8_t&gt;(</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>&#160; {</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>&#160; 7, 8, 9,</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>&#160; 10, 11, 12,</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>&#160; 13, 14, 15,</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>&#160; 16, 17, 18,</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>&#160;</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>&#160; 19, 20, 21,</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span>&#160; 22, 23, 24,</div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>&#160; 25, 26, 27,</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>&#160; 28, 29, 30,</div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span>&#160; 31, 32, 33,</div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>&#160; 34, 35, 36,</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>&#160;</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>&#160; 176, 177, 178,</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>&#160; 179, 181, 182,</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>&#160; 183, 184, 186,</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>&#160; 187, 188, 189,</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>&#160; 191, 192, 193,</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>&#160; 195, 196, 197,</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>&#160; })</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>&#160; );</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>&#160;</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>&#160; outputTensorInfo.SetQuantizationScale(outputScale);</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>&#160; outputTensorInfo.SetQuantizationOffset(outputOffset);</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>&#160; inputTensorInfo1.SetQuantizationScale(inputScale1);</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>&#160; inputTensorInfo1.SetQuantizationOffset(inputOffset1);</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>&#160; inputTensorInfo2.SetQuantizationScale(inputScale2);</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>&#160; inputTensorInfo2.SetQuantizationOffset(inputOffset2);</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>&#160;</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>&#160; std::vector&lt;unsigned int&gt; wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>&#160;</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>&#160; std::vector&lt;unsigned int&gt; wOrigin2 = { 2, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>&#160;</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>&#160;</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>&#160; <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>&#160;</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle1 =</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>&#160;</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle2 =</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>&#160;</div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>&#160; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>&#160; AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>&#160;</div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>&#160;</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>&#160;</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>&#160; inputHandle1-&gt;Allocate();</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>&#160; inputHandle2-&gt;Allocate();</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>&#160;</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &amp;input1[0][0][0]);</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &amp;input2[0][0][0]);</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>&#160;</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>&#160;</div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0], outputHandle.get());</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>&#160;</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00104">WorkloadData.hpp:104</a></div></div>
2955<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
2956<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ac043991b839903b2ba9da884e4020848"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">armnn::IWorkloadFactory::CreateSubTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateSubTensorHandle(ITensorHandle &amp;parent, TensorShape const &amp;subTensorShape, unsigned int const *subTensorOrigin) const =0</div></div>
2957<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
2958<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00102">WorkloadData.hpp:102</a></div></div>
2959<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a32bb8d6cf5fc028bf501252767c08b21"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">armnn::IWorkloadFactory::CreateConcat</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConcat(const ConcatQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01135">WorkloadFactory.cpp:1135</a></div></div>
2960<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00115">WorkloadData.hpp:115</a></div></div>
2961<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
2962<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
2963<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
2964<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
2965<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
2966<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
2967<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a37f4eba7877deb34f4d8d64c9bcb9ab5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">armnn::IWorkloadFactory::SupportsSubTensors</a></div><div class="ttdeci">virtual bool SupportsSubTensors() const =0</div></div>
2968</div><!-- fragment -->
2969</div>
2970</div>
2971<a id="ab0aa694e3cd5555731f28b2c61a01f7e"></a>
2972<h2 class="memtitle"><span class="permalink"><a href="#ab0aa694e3cd5555731f28b2c61a01f7e">&#9670;&nbsp;</a></span>ConcatUint8Test()</h2>
2973
2974<div class="memitem">
2975<div class="memproto">
2976 <table class="memname">
2977 <tr>
2978 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 3&gt; ConcatUint8Test </td>
2979 <td>(</td>
2980 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
2981 <td class="paramname"><em>workloadFactory</em>, </td>
2982 </tr>
2983 <tr>
2984 <td class="paramkey"></td>
2985 <td></td>
2986 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2987 <td class="paramname"><em>memoryManager</em>&#160;</td>
2988 </tr>
2989 <tr>
2990 <td></td>
2991 <td>)</td>
2992 <td></td><td></td>
2993 </tr>
2994 </table>
2995</div><div class="memdoc">
2996
2997<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l02497">2497</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
2998
2999<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01135">IWorkloadFactory::CreateConcat()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">IWorkloadFactory::CreateSubTensorHandle()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00115">ConcatQueueDescriptor::m_ViewOrigins</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>, and <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">IWorkloadFactory::SupportsSubTensors()</a>.</p>
3000<div class="fragment"><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>&#160;{</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>&#160;</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>&#160;</div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>&#160;</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>&#160;</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>&#160; <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QAsymmU8);</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QAsymmU8);</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QAsymmU8);</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>&#160;</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>&#160; <span class="comment">// Arbitrary scale and offsets. They don&#39;t really matter as the Concat operator doesn&#39;t dequantize/quantize them.</span></div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> scale = 0.13497836f;</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>&#160; <span class="keyword">const</span> int32_t offset = -7;</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>&#160;</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(scale);</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>&#160; outputTensorInfo.SetQuantizationOffset(offset);</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>&#160; inputTensorInfo1.SetQuantizationScale(scale);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>&#160; inputTensorInfo1.SetQuantizationOffset(offset);</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>&#160; inputTensorInfo2.SetQuantizationScale(scale);</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>&#160; inputTensorInfo2.SetQuantizationOffset(offset);</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>&#160;</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 3&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span>&#160;</div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>&#160; ret.outputExpected = MakeTensor&lt;uint8_t, 3&gt;(outputTensorInfo, std::vector&lt;uint8_t&gt;(</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>&#160; {</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>&#160; 7, 8, 9,</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>&#160; 10, 11, 12,</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>&#160; 13, 14, 15,</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>&#160; 16, 17, 18,</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>&#160;</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>&#160; 19, 20, 21,</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>&#160; 22, 23, 24,</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>&#160; 25, 26, 27,</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>&#160; 28, 29, 30,</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>&#160; 31, 32, 33,</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>&#160; 34, 35, 36,</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>&#160;</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>&#160; 37, 38, 39,</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>&#160; 40, 41, 42,</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>&#160; 43, 44, 45,</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>&#160; 46, 47, 48,</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>&#160; 49, 50, 51,</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>&#160; 52, 53, 54,</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>&#160; })</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>&#160; );</div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>&#160;</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;uint8_t, 3&gt;(inputTensorInfo1, std::vector&lt;uint8_t&gt;(</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>&#160; {</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>&#160; 7, 8, 9,</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>&#160; 10, 11, 12,</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>&#160; 13, 14, 15,</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>&#160; 16, 17, 18,</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>&#160;</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>&#160; 19, 20, 21,</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>&#160; 22, 23, 24,</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>&#160; 25, 26, 27,</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>&#160; 28, 29, 30,</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>&#160; 31, 32, 33,</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>&#160; 34, 35, 36,</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>&#160; })</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>&#160; );</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>&#160;</div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;uint8_t, 3&gt;(inputTensorInfo2, std::vector&lt;uint8_t&gt;(</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>&#160; {</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>&#160; 37, 38, 39,</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>&#160; 40, 41, 42,</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>&#160; 43, 44, 45,</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>&#160; 46, 47, 48,</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>&#160; 49, 50, 51,</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>&#160; 52, 53, 54,</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>&#160; })</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>&#160; );</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>&#160;</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>&#160; std::vector&lt;unsigned int&gt; wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>&#160;</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>&#160; std::vector&lt;unsigned int&gt; wOrigin2 = { 2, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>&#160; <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>&#160;</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>&#160;</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>&#160;</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>&#160; <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>&#160;</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle1 =</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>&#160; subTensorsSupported ?</div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>&#160; workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>&#160;</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>&#160; 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<a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>&#160; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>&#160; AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>&#160;</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>&#160; data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>&#160;</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>&#160;</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>&#160; inputHandle1-&gt;Allocate();</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>&#160; inputHandle2-&gt;Allocate();</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>&#160;</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>&#160; 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<span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00104">WorkloadData.hpp:104</a></div></div>
3001<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
3002<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_ac043991b839903b2ba9da884e4020848"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#ac043991b839903b2ba9da884e4020848">armnn::IWorkloadFactory::CreateSubTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateSubTensorHandle(ITensorHandle &amp;parent, TensorShape const &amp;subTensorShape, unsigned int const *subTensorOrigin) const =0</div></div>
3003<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
3004<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00102">WorkloadData.hpp:102</a></div></div>
3005<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a32bb8d6cf5fc028bf501252767c08b21"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">armnn::IWorkloadFactory::CreateConcat</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConcat(const ConcatQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01135">WorkloadFactory.cpp:1135</a></div></div>
3006<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00115">WorkloadData.hpp:115</a></div></div>
3007<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00259">Tensor.cpp:259</a></div></div>
3008<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
3009<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
3010<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
3011<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
3012<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
3013<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
3014<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a37f4eba7877deb34f4d8d64c9bcb9ab5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">armnn::IWorkloadFactory::SupportsSubTensors</a></div><div class="ttdeci">virtual bool SupportsSubTensors() const =0</div></div>
3015</div><!-- fragment -->
3016</div>
3017</div>
3018<a id="a908c80ff86d48fe1bc7cd4d4b1d00147"></a>
3019<h2 class="memtitle"><span class="permalink"><a href="#a908c80ff86d48fe1bc7cd4d4b1d00147">&#9670;&nbsp;</a></span>CreateDescriptorForConcat()</h2>
3020
3021<div class="memitem">
3022<div class="memproto">
3023 <table class="memname">
3024 <tr>
3025 <td class="memname"><a class="el" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> CreateDescriptorForConcat </td>
3026 <td>(</td>
3027 <td class="paramtype">const std::vector&lt; <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt; &amp;&#160;</td>
3028 <td class="paramname"><em>inputTensorInfos</em>, </td>
3029 </tr>
3030 <tr>
3031 <td class="paramkey"></td>
3032 <td></td>
3033 <td class="paramtype">unsigned int&#160;</td>
3034 <td class="paramname"><em>concatDim</em>&#160;</td>
3035 </tr>
3036 <tr>
3037 <td></td>
3038 <td>)</td>
3039 <td></td><td></td>
3040 </tr>
3041 </table>
3042</div><div class="memdoc">
3043
3044<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00026">26</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
3045
3046<p class="reference">References <a class="el" href="_descriptors_8hpp_source.xhtml#l00242">armnn::CreateDescriptorForConcatenation()</a>.</p>
3047
3048<p class="reference">Referenced by <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00272">Concatenate()</a>.</p>
3049<div class="fragment"><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;{</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; std::vector&lt;TensorShape&gt; shapes;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; shapes.reserve(inputTensorInfos.size());</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keywordflow">for</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>&amp; it: inputTensorInfos)</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; shapes.push_back(it.GetShape());</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; }</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), concatDim);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
3050<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00242">Descriptors.hpp:242</a></div></div>
3051</div><!-- fragment -->
3052</div>
3053</div>
3054<a id="a8fcf10f2804bcbbfef4fd86ef6a5ff2d"></a>
3055<h2 class="memtitle"><span class="permalink"><a href="#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">&#9670;&nbsp;</a></span>ExpandTensorShapeTo3dForPermute()</h2>
3056
3057<div class="memitem">
3058<div class="memproto">
3059 <table class="memname">
3060 <tr>
3061 <td class="memname"><a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> ExpandTensorShapeTo3dForPermute </td>
3062 <td>(</td>
3063 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> &amp;&#160;</td>
3064 <td class="paramname"><em>inputShape</em></td><td>)</td>
3065 <td></td>
3066 </tr>
3067 </table>
3068</div><div class="memdoc">
3069
3070<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00072">72</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
3071
3072<p class="reference">References <a class="el" href="_tensor_8hpp_source.xhtml#l00043">TensorShape::GetNumDimensions()</a>.</p>
3073
3074<p class="reference">Referenced by <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00171">PermuteInputsForConcat()</a>.</p>
3075<div class="fragment"><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;{</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims = inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <span class="keywordflow">if</span> (numDims &gt;= 3)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="comment">// Nothing to do if the inputShape has at least 3 dimensions.</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keywordflow">return</span> inputShape;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; std::vector&lt;unsigned int&gt; newDims(<span class="keywordtype">size_t</span>(3), 1u);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expandedBy = 3 - numDims;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i&lt;numDims; ++i)</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; newDims[expandedBy+i] = inputShape[i];</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(3u, &amp;newDims[0]);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
3076<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00043">Tensor.hpp:43</a></div></div>
3077</div><!-- fragment -->
3078</div>
3079</div>
3080<a id="abd92409a35f1f4c41ee52c7471936fd8"></a>
3081<h2 class="memtitle"><span class="permalink"><a href="#abd92409a35f1f4c41ee52c7471936fd8">&#9670;&nbsp;</a></span>Generate3dPermuteVectorForConcat()</h2>
3082
3083<div class="memitem">
3084<div class="memproto">
3085 <table class="memname">
3086 <tr>
3087 <td class="memname">void Generate3dPermuteVectorForConcat </td>
3088 <td>(</td>
3089 <td class="paramtype">unsigned int&#160;</td>
3090 <td class="paramname"><em>numDimensions</em>, </td>
3091 </tr>
3092 <tr>
3093 <td class="paramkey"></td>
3094 <td></td>
3095 <td class="paramtype">unsigned int &amp;&#160;</td>
3096 <td class="paramname"><em>concatDim</em>, </td>
3097 </tr>
3098 <tr>
3099 <td class="paramkey"></td>
3100 <td></td>
3101 <td class="paramtype">std::pair&lt; <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>, <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &gt; &amp;&#160;</td>
3102 <td class="paramname"><em>permutations</em>&#160;</td>
3103 </tr>
3104 <tr>
3105 <td></td>
3106 <td>)</td>
3107 <td></td><td></td>
3108 </tr>
3109 </table>
3110</div><div class="memdoc">
3111
3112<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00090">90</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
3113
3114<p class="reference">Referenced by <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00171">PermuteInputsForConcat()</a>.</p>
3115<div class="fragment"><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160;{</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; BOOST_ASSERT_MSG(numDimensions &lt;= 3,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="stringliteral">&quot;Only dimensions 1,2 and 3 are supported by this helper&quot;</span>);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expandedBy = 3 - numDimensions;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expandedConcatAxis = concatDim + expandedBy;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="keywordflow">if</span> (expandedConcatAxis == 2)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; concatDim = 0;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> forwardPermutation({1, 2, 0});</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> reversePermutation({2, 0, 1});</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; permutations = std::make_pair(forwardPermutation, reversePermutation);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="keywordflow">else</span> <span class="keywordflow">if</span> (expandedConcatAxis == 1)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; {</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; concatDim = 0;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> forwardPermutation({2, 0, 1});</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> reversePermutation({1, 2, 0});</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; permutations = std::make_pair(forwardPermutation, reversePermutation);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; BOOST_ASSERT(expandedConcatAxis == 0);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; concatDim = 0;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; }</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00173">Types.hpp:173</a></div></div>
3116</div><!-- fragment -->
3117</div>
3118</div>
3119<a id="a905e011ae8536bbd643dd09495524596"></a>
3120<h2 class="memtitle"><span class="permalink"><a href="#a905e011ae8536bbd643dd09495524596">&#9670;&nbsp;</a></span>NeedPermuteForConcat()</h2>
3121
3122<div class="memitem">
3123<div class="memproto">
3124 <table class="memname">
3125 <tr>
3126 <td class="memname">bool NeedPermuteForConcat </td>
3127 <td>(</td>
3128 <td class="paramtype">const std::vector&lt; <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt; &amp;&#160;</td>
3129 <td class="paramname"><em>inputTensorInfos</em>, </td>
3130 </tr>
3131 <tr>
3132 <td class="paramkey"></td>
3133 <td></td>
3134 <td class="paramtype">unsigned int&#160;</td>
3135 <td class="paramname"><em>concatDim</em>&#160;</td>
3136 </tr>
3137 <tr>
3138 <td></td>
3139 <td>)</td>
3140 <td></td><td></td>
3141 </tr>
3142 </table>
3143</div><div class="memdoc">
3144
3145<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00046">46</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
3146
3147<p class="reference">Referenced by <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00272">Concatenate()</a>.</p>
3148<div class="fragment"><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;{</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <span class="comment">// See note above. Additionally we expect the input shapes to have the</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="comment">// same number of dimensions.</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nDimensions = 0;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <span class="comment">// Determine the number of dimensions as well as sanity check them</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="comment">// agains test implementation issues.</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="keywordflow">for</span> (<span class="keyword">auto</span> &amp;&amp; tensorInfo : inputTensorInfos)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="keywordflow">if</span> (!nDimensions)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; nDimensions = tensorInfo.GetShape().GetNumDimensions();</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; }</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(),</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="stringliteral">&quot;Input shapes must have the same number of dimensions&quot;</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; }</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="keywordflow">return</span> (nDimensions &lt; 3 || (nDimensions == 3 &amp;&amp; (nDimensions-concatDim) &lt; 3 &amp;&amp; (nDimensions-concatDim) != 1));</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;}</div></div><!-- fragment -->
3149</div>
3150</div>
3151<a id="a501616a77a3c7ca6d809c52e52da6ae3"></a>
3152<h2 class="memtitle"><span class="permalink"><a href="#a501616a77a3c7ca6d809c52e52da6ae3">&#9670;&nbsp;</a></span>PermuteInputsForConcat()</h2>
3153
3154<div class="memitem">
3155<div class="memproto">
3156 <table class="memname">
3157 <tr>
3158 <td class="memname">void PermuteInputsForConcat </td>
3159 <td>(</td>
3160 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
3161 <td class="paramname"><em>workloadFactory</em>, </td>
3162 </tr>
3163 <tr>
3164 <td class="paramkey"></td>
3165 <td></td>
3166 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3167 <td class="paramname"><em>memoryManager</em>, </td>
3168 </tr>
3169 <tr>
3170 <td class="paramkey"></td>
3171 <td></td>
3172 <td class="paramtype">std::vector&lt; <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &gt; &amp;&#160;</td>
3173 <td class="paramname"><em>inputTensorInfos</em>, </td>
3174 </tr>
3175 <tr>
3176 <td class="paramkey"></td>
3177 <td></td>
3178 <td class="paramtype">std::vector&lt; T *&gt; &amp;&#160;</td>
3179 <td class="paramname"><em>inputData</em>, </td>
3180 </tr>
3181 <tr>
3182 <td class="paramkey"></td>
3183 <td></td>
3184 <td class="paramtype">std::vector&lt; std::vector&lt; T &gt;&gt; &amp;&#160;</td>
3185 <td class="paramname"><em>inputDataStorage</em>, </td>
3186 </tr>
3187 <tr>
3188 <td class="paramkey"></td>
3189 <td></td>
3190 <td class="paramtype"><a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &amp;&#160;</td>
3191 <td class="paramname"><em>permuteVector</em>, </td>
3192 </tr>
3193 <tr>
3194 <td class="paramkey"></td>
3195 <td></td>
3196 <td class="paramtype">unsigned int &amp;&#160;</td>
3197 <td class="paramname"><em>concatDim</em>, </td>
3198 </tr>
3199 <tr>
3200 <td class="paramkey"></td>
3201 <td></td>
3202 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;&#160;</td>
3203 <td class="paramname"><em>outputTensorInfo</em>&#160;</td>
3204 </tr>
3205 <tr>
3206 <td></td>
3207 <td>)</td>
3208 <td></td><td></td>
3209 </tr>
3210 </table>
3211</div><div class="memdoc">
3212
3213<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00171">171</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
3214
3215<p class="reference">References <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00072">ExpandTensorShapeTo3dForPermute()</a>, <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00090">Generate3dPermuteVectorForConcat()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="_types_8hpp_source.xhtml#l00207">PermutationVector::IsEqual()</a>, <a class="el" href="_permute_8cpp_source.xhtml#l00098">armnnUtils::Permuted()</a>, and <a class="el" href="_tensor_8hpp_source.xhtml#l00090">TensorInfo::SetShape()</a>.</p>
3216<div class="fragment"><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;{</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; BOOST_ASSERT_MSG(inputTensorInfos.size() &gt; 1,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <span class="stringliteral">&quot;Expecting more than one tensor to be concatenated here&quot;</span>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims = 0;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nthInput = 0;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> identity({0, 1, 2});</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; std::pair&lt;PermutationVector, PermutationVector&gt; permutations =</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; std::make_pair(identity, identity);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; inputDataStorage.resize(inputData.size());</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160;</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; <span class="keywordflow">for</span> (<span class="keyword">auto</span> &amp;&amp; tensorInfo : inputTensorInfos)</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="keywordflow">if</span> (numDims == 0)</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; numDims = tensorInfo.GetShape().GetNumDimensions();</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; <a class="code" href="_concat_test_impl_8cpp.xhtml#abd92409a35f1f4c41ee52c7471936fd8">Generate3dPermuteVectorForConcat</a>(numDims, concatDim, permutations);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160;</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <span class="comment">// Store the reverese permutation.</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; permuteVector = permutations.second;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; BOOST_ASSERT_MSG(!permuteVector.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#aae44e4154aa80fba7616747450ff69d5">IsEqual</a>(identity),</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; <span class="stringliteral">&quot;Test logic error, we don&#39;t need permutation, so we shouldn&#39;t arrive here&quot;</span>);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(),</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <span class="stringliteral">&quot;All inputs must have the same number of dimensions&quot;</span>);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; }</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> newTensorInfo = tensorInfo;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; newTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a>(tensorInfo.GetShape()));</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160;</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; PermuteTensorData&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; memoryManager,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; permutations.first,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; newTensorInfo,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; inputData[nthInput],</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; inputDataStorage[nthInput]);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; inputData[nthInput] = inputDataStorage[nthInput].data();</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; inputTensorInfos[nthInput] = newTensorInfo;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; ++nthInput;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; <a class="code" href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()),</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; permutations.first));</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div>
3217<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
3218<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
3219<div class="ttc" id="_concat_test_impl_8cpp_xhtml_abd92409a35f1f4c41ee52c7471936fd8"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#abd92409a35f1f4c41ee52c7471936fd8">Generate3dPermuteVectorForConcat</a></div><div class="ttdeci">void Generate3dPermuteVectorForConcat(unsigned int numDimensions, unsigned int &amp;concatDim, std::pair&lt; PermutationVector, PermutationVector &gt; &amp;permutations)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00090">ConcatTestImpl.cpp:90</a></div></div>
3220<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &amp;newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00090">Tensor.hpp:90</a></div></div>
3221<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00173">Types.hpp:173</a></div></div>
3222<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a8fcf10f2804bcbbfef4fd86ef6a5ff2d"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a></div><div class="ttdeci">TensorShape ExpandTensorShapeTo3dForPermute(const TensorShape &amp;inputShape)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00072">ConcatTestImpl.cpp:72</a></div></div>
3223<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml_aae44e4154aa80fba7616747450ff69d5"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml#aae44e4154aa80fba7616747450ff69d5">armnn::PermutationVector::IsEqual</a></div><div class="ttdeci">bool IsEqual(const PermutationVector &amp;other) const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00207">Types.hpp:207</a></div></div>
3224<div class="ttc" id="namespacearmnn_utils_xhtml_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &amp;srcShape, const armnn::PermutationVector &amp;mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00098">Permute.cpp:98</a></div></div>
3225</div><!-- fragment -->
3226</div>
3227</div>
3228<a id="a46079932a4f92d02da9b0b538ddca52c"></a>
3229<h2 class="memtitle"><span class="permalink"><a href="#a46079932a4f92d02da9b0b538ddca52c">&#9670;&nbsp;</a></span>PermuteOutputForConcat()</h2>
3230
3231<div class="memitem">
3232<div class="memproto">
3233 <table class="memname">
3234 <tr>
3235 <td class="memname">void PermuteOutputForConcat </td>
3236 <td>(</td>
3237 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
3238 <td class="paramname"><em>workloadFactory</em>, </td>
3239 </tr>
3240 <tr>
3241 <td class="paramkey"></td>
3242 <td></td>
3243 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3244 <td class="paramname"><em>memoryManager</em>, </td>
3245 </tr>
3246 <tr>
3247 <td class="paramkey"></td>
3248 <td></td>
3249 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;&#160;</td>
3250 <td class="paramname"><em>tensorInfo</em>, </td>
3251 </tr>
3252 <tr>
3253 <td class="paramkey"></td>
3254 <td></td>
3255 <td class="paramtype">const <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &amp;&#160;</td>
3256 <td class="paramname"><em>permuteVector</em>, </td>
3257 </tr>
3258 <tr>
3259 <td class="paramkey"></td>
3260 <td></td>
3261 <td class="paramtype">std::unique_ptr&lt; <a class="el" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a> &gt; &amp;&amp;&#160;</td>
3262 <td class="paramname"><em>inputDataHandle</em>, </td>
3263 </tr>
3264 <tr>
3265 <td class="paramkey"></td>
3266 <td></td>
3267 <td class="paramtype">T *&#160;</td>
3268 <td class="paramname"><em>data</em>&#160;</td>
3269 </tr>
3270 <tr>
3271 <td></td>
3272 <td>)</td>
3273 <td></td><td></td>
3274 </tr>
3275 </table>
3276</div><div class="memdoc">
3277
3278<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00239">239</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
3279
3280<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, and <a class="el" href="_tensor_8hpp_source.xhtml#l00093">TensorInfo::GetNumElements()</a>.</p>
3281<div class="fragment"><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160;{</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; BOOST_ASSERT_MSG(data != <span class="keyword">nullptr</span>, <span class="stringliteral">&quot;data must not be null&quot;</span>);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <span class="keywordflow">if</span> (data == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <span class="comment">// Nullptr is an error in the test. By returning without doing the permutation</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <span class="comment">// I expect the caller to fail the test. It still makes sense to report this as</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; <span class="comment">// an assert for Debug builds.</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <span class="keywordflow">return</span>;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; }</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160;</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> resultTensorInfo = tensorInfo;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; std::vector&lt;T&gt; inputData(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; std::vector&lt;T&gt; outputData;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;inputData[0], inputDataHandle.get());</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; PermuteTensorData&lt;T&gt;(workloadFactory,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; memoryManager,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; permuteVector,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; resultTensorInfo,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; &amp;inputData[0],</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; outputData);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160;</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; ::memcpy(data, &amp;outputData[0], <span class="keyword">sizeof</span>(T)*outputData.size());</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
3282<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
3283<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00093">Tensor.hpp:93</a></div></div>
3284</div><!-- fragment -->
3285</div>
3286</div>
3287<a id="a64d353b468c3a9ec4b783a06cf59cb42"></a>
3288<h2 class="memtitle"><span class="permalink"><a href="#a64d353b468c3a9ec4b783a06cf59cb42">&#9670;&nbsp;</a></span>PermuteTensorData()</h2>
3289
3290<div class="memitem">
3291<div class="memproto">
3292 <table class="memname">
3293 <tr>
3294 <td class="memname">void PermuteTensorData </td>
3295 <td>(</td>
3296 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a> &amp;&#160;</td>
3297 <td class="paramname"><em>workloadFactory</em>, </td>
3298 </tr>
3299 <tr>
3300 <td class="paramkey"></td>
3301 <td></td>
3302 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3303 <td class="paramname"><em>memoryManager</em>, </td>
3304 </tr>
3305 <tr>
3306 <td class="paramkey"></td>
3307 <td></td>
3308 <td class="paramtype">const <a class="el" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> &amp;&#160;</td>
3309 <td class="paramname"><em>mappings</em>, </td>
3310 </tr>
3311 <tr>
3312 <td class="paramkey"></td>
3313 <td></td>
3314 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &amp;&#160;</td>
3315 <td class="paramname"><em>inputTensorInfo</em>, </td>
3316 </tr>
3317 <tr>
3318 <td class="paramkey"></td>
3319 <td></td>
3320 <td class="paramtype">const T *&#160;</td>
3321 <td class="paramname"><em>inputData</em>, </td>
3322 </tr>
3323 <tr>
3324 <td class="paramkey"></td>
3325 <td></td>
3326 <td class="paramtype">std::vector&lt; T &gt; &amp;&#160;</td>
3327 <td class="paramname"><em>outputData</em>&#160;</td>
3328 </tr>
3329 <tr>
3330 <td></td>
3331 <td>)</td>
3332 <td></td><td></td>
3333 </tr>
3334 </table>
3335</div><div class="memdoc">
3336
3337<p class="definition">Definition at line <a class="el" href="_concat_test_impl_8cpp_source.xhtml#l00121">121</a> of file <a class="el" href="_concat_test_impl_8cpp_source.xhtml">ConcatTestImpl.cpp</a>.</p>
3338
3339<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01334">IWorkloadFactory::CreatePermute()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00093">TensorInfo::GetNumElements()</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, and <a class="el" href="_permute_8cpp_source.xhtml#l00098">armnnUtils::Permuted()</a>.</p>
3340<div class="fragment"><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;{</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; BOOST_ASSERT_MSG(inputData != <span class="keyword">nullptr</span>, <span class="stringliteral">&quot;inputData must not be null&quot;</span>);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; <span class="keywordflow">if</span> (inputData == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <span class="comment">// Nullptr is an error in the test. By returning without doing the concatenation</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <span class="comment">// I expect the caller to fail the test. It still makes sense to report this as</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="comment">// an assert for Debug builds.</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <span class="keywordflow">return</span>;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; }</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(inputTensorInfo, mappings);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <a class="code" href="structarmnn_1_1_permute_queue_descriptor.xhtml">PermuteQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = <a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a>{mappings};</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2dcee0bc4bbae1f745324aed0f841a0a">CreatePermute</a>(queueDescriptor, workloadInfo);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; outputData.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;outputData[0], outputHandle.get());</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; inputTensorInfo = outputTensorInfo;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_permute_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_queue_descriptor.xhtml">armnn::PermuteQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00156">WorkloadData.hpp:156</a></div></div>
3341<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
3342<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
3343<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div>
3344<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
3345<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
3346<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2dcee0bc4bbae1f745324aed0f841a0a"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2dcee0bc4bbae1f745324aed0f841a0a">armnn::IWorkloadFactory::CreatePermute</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreatePermute(const PermuteQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01334">WorkloadFactory.cpp:1334</a></div></div>
3347<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
3348<div class="ttc" id="namespacearmnn_utils_xhtml_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &amp;srcShape, const armnn::PermutationVector &amp;mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00098">Permute.cpp:98</a></div></div>
3349<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00093">Tensor.hpp:93</a></div></div>
3350<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00102">Descriptors.hpp:102</a></div></div>
3351<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
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3353</div>
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3359 <ul>
3360 <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_797a213d7d01b98ef12d53b0820ea64e.xhtml">backendsCommon</a></li><li class="navelem"><a class="el" href="dir_28bfe507f7e135bdae07c2a6b7f66696.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_99a30439342d160875b21dac3498ad7f.xhtml">layerTests</a></li><li class="navelem"><a class="el" href="_concat_test_impl_8cpp.xhtml">ConcatTestImpl.cpp</a></li>
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