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review.mlplatform.org / ml / armnn / cc34093a57ea486e48f9aa66fc8b98a7bbdefef1 / . / Documentation / _conv2d_test_impl_8cpp.xhtml
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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">Conv2dTestImpl.cpp File Reference</div> </div>
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103<div class="textblock"><code>#include &quot;<a class="el" href="_conv2d_test_impl_8hpp_source.xhtml">Conv2dTestImpl.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="_tensor_utils_8hpp_source.xhtml">armnnUtils/TensorUtils.hpp</a>&gt;</code><br />
106<code>#include &lt;<a class="el" href="_ignore_unused_8hpp_source.xhtml">armnn/utility/IgnoreUnused.hpp</a>&gt;</code><br />
107<code>#include &lt;<a class="el" href="_data_layout_indexed_8hpp_source.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>&gt;</code><br />
108<code>#include &lt;<a class="el" href="_permute_8hpp_source.xhtml">armnnUtils/Permute.hpp</a>&gt;</code><br />
109<code>#include &lt;<a class="el" href="_cpu_tensor_handle_8hpp_source.xhtml">backendsCommon/CpuTensorHandle.hpp</a>&gt;</code><br />
110<code>#include &lt;<a class="el" href="_data_layout_utils_8hpp_source.xhtml">backendsCommon/test/DataLayoutUtils.hpp</a>&gt;</code><br />
111<code>#include &lt;<a class="el" href="_tensor_copy_utils_8hpp_source.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>&gt;</code><br />
112<code>#include &lt;<a class="el" href="_workload_test_utils_8hpp_source.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>&gt;</code><br />
113<code>#include &lt;<a class="el" href="_tensor_helpers_8hpp_source.xhtml">test/TensorHelpers.hpp</a>&gt;</code><br />
114<code>#include &lt;boost/numeric/conversion/cast.hpp&gt;</code><br />
115<code>#include &lt;string&gt;</code><br />
116</div>
117<p><a href="_conv2d_test_impl_8cpp_source.xhtml">Go to the source code of this file.</a></p>
118<table class="memberdecls">
119<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
120Functions</h2></td></tr>
121<tr class="memitem:ad80bc46727797692d35f94d5935469cb"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
122<tr class="memitem:ad80bc46727797692d35f94d5935469cb"><td class="memTemplItemLeft" align="right" valign="top">boost::multi_array&lt; T, 1 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#ad80bc46727797692d35f94d5935469cb">GetBias2</a> (bool biasEnabled, float qScale)</td></tr>
123<tr class="separator:ad80bc46727797692d35f94d5935469cb"><td class="memSeparator" colspan="2">&#160;</td></tr>
124<tr class="memitem:aa794621b8665d1df93a1c9aa95d5a90d"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
125<tr class="memitem:aa794621b8665d1df93a1c9aa95d5a90d"><td class="memTemplItemLeft" align="right" valign="top">boost::multi_array&lt; T, 1 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#aa794621b8665d1df93a1c9aa95d5a90d">GetBias4</a> (bool biasEnabled, float qScale)</td></tr>
126<tr class="separator:aa794621b8665d1df93a1c9aa95d5a90d"><td class="memSeparator" colspan="2">&#160;</td></tr>
127<tr class="memitem:ae04bff4e44deed6908feae29e57ffe0c"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
128<tr class="memitem:ae04bff4e44deed6908feae29e57ffe0c"><td class="memTemplItemLeft" align="right" valign="top">boost::multi_array&lt; T, 1 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#ae04bff4e44deed6908feae29e57ffe0c">GetBias8</a> (bool biasEnabled, float qScale)</td></tr>
129<tr class="separator:ae04bff4e44deed6908feae29e57ffe0c"><td class="memSeparator" colspan="2">&#160;</td></tr>
130<tr class="memitem:a3481304dfd3e941b809c64979b940ad5"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
131<tr class="memitem:a3481304dfd3e941b809c64979b940ad5"><td class="memTemplItemLeft" align="right" valign="top">boost::multi_array&lt; T, 1 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a3481304dfd3e941b809c64979b940ad5">GetBias</a> (bool biasEnabled, float qScale, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
132<tr class="separator:a3481304dfd3e941b809c64979b940ad5"><td class="memSeparator" colspan="2">&#160;</td></tr>
133<tr class="memitem:aa1f4ce02e0904dc8cf1b7f42bc34d346"><td class="memTemplParams" colspan="2">template&lt;typename T , typename B &gt; </td></tr>
134<tr class="memitem:aa1f4ce02e0904dc8cf1b7f42bc34d346"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a> (std::vector&lt; T &gt; &amp;v, float vScale, int32_t vOffset, const std::vector&lt; B &gt; &amp;bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</td></tr>
135<tr class="separator:aa1f4ce02e0904dc8cf1b7f42bc34d346"><td class="memSeparator" colspan="2">&#160;</td></tr>
136<tr class="memitem:a7bd1547ceefdc1acedbb1fa6445b2968"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;, typename B = armnn::ResolveType&lt;ArmnnBType&gt;&gt; </td></tr>
137<tr class="memitem:a7bd1547ceefdc1acedbb1fa6445b2968"><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="_conv2d_test_impl_8cpp.xhtml#a7bd1547ceefdc1acedbb1fa6445b2968">SimpleConvolution2dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const boost::multi_array&lt; T, 4 &gt; &amp;originalInput, const boost::multi_array&lt; T, 4 &gt; &amp;originalKernel, const boost::multi_array&lt; B, 1 &gt; &amp;bias, const boost::multi_array&lt; T, 4 &gt; &amp;originalOutputExpected, float qScale, int32_t qOffset, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout=<a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>, uint32_t padLeft=0, uint32_t padTop=0, uint32_t padRight=0, uint32_t padBottom=0, uint32_t strideX=1, uint32_t strideY=1, uint32_t dilationX=1, uint32_t dilationY=1)</td></tr>
138<tr class="separator:a7bd1547ceefdc1acedbb1fa6445b2968"><td class="memSeparator" colspan="2">&#160;</td></tr>
139<tr class="memitem:ac79e75b3bcb6cb8c34f0bd4e3e35f73e"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;, typename B = armnn::ResolveType&lt;ArmnnBType&gt;&gt; </td></tr>
140<tr class="memitem:ac79e75b3bcb6cb8c34f0bd4e3e35f73e"><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="_conv2d_test_impl_8cpp.xhtml#ac79e75b3bcb6cb8c34f0bd4e3e35f73e">SimpleConvolution2dNhwcTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const boost::multi_array&lt; T, 4 &gt; &amp;input, const boost::multi_array&lt; T, 4 &gt; &amp;kernel, const boost::multi_array&lt; B, 1 &gt; &amp;bias, const boost::multi_array&lt; T, 4 &gt; &amp;outputExpected, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout, float qScale, int32_t qOffset, uint32_t padLeft=1, uint32_t padTop=1, uint32_t padRight=1, uint32_t padBottom=1, uint32_t strideX=1, uint32_t strideY=1)</td></tr>
141<tr class="separator:ac79e75b3bcb6cb8c34f0bd4e3e35f73e"><td class="memSeparator" colspan="2">&#160;</td></tr>
142<tr class="memitem:af541f19e3d1ad345cc9208fc2d2e7b19"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
143<tr class="memitem:af541f19e3d1ad345cc9208fc2d2e7b19"><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="_conv2d_test_impl_8cpp.xhtml#af541f19e3d1ad345cc9208fc2d2e7b19">Convolution1dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled)</td></tr>
144<tr class="separator:af541f19e3d1ad345cc9208fc2d2e7b19"><td class="memSeparator" colspan="2">&#160;</td></tr>
145<tr class="memitem:a8225effadfc56a5d831ae0f7f686a6cf"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
146<tr class="memitem:a8225effadfc56a5d831ae0f7f686a6cf"><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="_conv2d_test_impl_8cpp.xhtml#a8225effadfc56a5d831ae0f7f686a6cf">SimpleConvolution2d3x3NhwcTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</td></tr>
147<tr class="separator:a8225effadfc56a5d831ae0f7f686a6cf"><td class="memSeparator" colspan="2">&#160;</td></tr>
148<tr class="memitem:aafa5b575d2bc27ec7229f1d87ab8efdb"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
149<tr class="memitem:aafa5b575d2bc27ec7229f1d87ab8efdb"><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="_conv2d_test_impl_8cpp.xhtml#aafa5b575d2bc27ec7229f1d87ab8efdb">SimpleConvolution2d3x3Stride2x2TestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> &amp;dataLayout)</td></tr>
150<tr class="separator:aafa5b575d2bc27ec7229f1d87ab8efdb"><td class="memSeparator" colspan="2">&#160;</td></tr>
151<tr class="memitem:a3660079f1e20e5b1618402dfc5214441"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
152<tr class="memitem:a3660079f1e20e5b1618402dfc5214441"><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="_conv2d_test_impl_8cpp.xhtml#a3660079f1e20e5b1618402dfc5214441">SimpleConvolution2d3x5TestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
153<tr class="separator:a3660079f1e20e5b1618402dfc5214441"><td class="memSeparator" colspan="2">&#160;</td></tr>
154<tr class="memitem:a5070a9bac7ba582ed116a8b2323ed2a5"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
155<tr class="memitem:a5070a9bac7ba582ed116a8b2323ed2a5"><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="_conv2d_test_impl_8cpp.xhtml#a5070a9bac7ba582ed116a8b2323ed2a5">SimpleConvolution2d3x3TestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
156<tr class="separator:a5070a9bac7ba582ed116a8b2323ed2a5"><td class="memSeparator" colspan="2">&#160;</td></tr>
157<tr class="memitem:a35ad1225c524b4594b461e613695ee4a"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
158<tr class="memitem:a35ad1225c524b4594b461e613695ee4a"><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="_conv2d_test_impl_8cpp.xhtml#a35ad1225c524b4594b461e613695ee4a">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout, float qScale, int32_t qOffset)</td></tr>
159<tr class="separator:a35ad1225c524b4594b461e613695ee4a"><td class="memSeparator" colspan="2">&#160;</td></tr>
160<tr class="memitem:af32b0642214e3129d8e93fa45a12e704"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
161<tr class="memitem:af32b0642214e3129d8e93fa45a12e704"><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="_conv2d_test_impl_8cpp.xhtml#af32b0642214e3129d8e93fa45a12e704">SimpleConvolution2dAsymmetricPaddingTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout, float qScale, int32_t qOffset)</td></tr>
162<tr class="separator:af32b0642214e3129d8e93fa45a12e704"><td class="memSeparator" colspan="2">&#160;</td></tr>
163<tr class="memitem:ad12c52b6d41931219bdfec5fbf5990bd"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
164<tr class="memitem:ad12c52b6d41931219bdfec5fbf5990bd"><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="_conv2d_test_impl_8cpp.xhtml#ad12c52b6d41931219bdfec5fbf5990bd">Convolution2d3x3DilationTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const std::vector&lt; float &gt; &amp;inputNoQuantizedValues, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;inputTensorInfo, const std::vector&lt; float &gt; &amp;kernelNoQuantizedValues, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;kernelTensorInfo, const std::vector&lt; float &gt; &amp;outputExpectedNoQuantizedValues, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;outputTensorInfo, uint32_t dilationX, uint32_t dilationY, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout=<a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>, uint32_t padLeft=0, uint32_t padTop=0, uint32_t padRight=0, uint32_t padBottom=0, uint32_t strideX=1, uint32_t strideY=1, bool biasEnabled=<a class="el" href="_ref_layer_tests_8cpp.xhtml#af3b727ae5a13ff472892ab8bda2eb1b5">false</a>)</td></tr>
165<tr class="separator:ad12c52b6d41931219bdfec5fbf5990bd"><td class="memSeparator" colspan="2">&#160;</td></tr>
166<tr class="memitem:a90abce368d7f16012bef5ee461329484"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T &gt; </td></tr>
167<tr class="memitem:a90abce368d7f16012bef5ee461329484"><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="_conv2d_test_impl_8cpp.xhtml#a90abce368d7f16012bef5ee461329484">Convolution2d3x3Dilation3x3Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
168<tr class="separator:a90abce368d7f16012bef5ee461329484"><td class="memSeparator" colspan="2">&#160;</td></tr>
169<tr class="memitem:a99ef3f48cbd057e0169bc80dc77331ef"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T &gt; </td></tr>
170<tr class="memitem:a99ef3f48cbd057e0169bc80dc77331ef"><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="_conv2d_test_impl_8cpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef">Convolution2d2x3x3Dilation3x3Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
171<tr class="separator:a99ef3f48cbd057e0169bc80dc77331ef"><td class="memSeparator" colspan="2">&#160;</td></tr>
172<tr class="memitem:acf553288e3b5060768fb91e064993678"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T &gt; </td></tr>
173<tr class="memitem:acf553288e3b5060768fb91e064993678"><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="_conv2d_test_impl_8cpp.xhtml#acf553288e3b5060768fb91e064993678">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
174<tr class="separator:acf553288e3b5060768fb91e064993678"><td class="memSeparator" colspan="2">&#160;</td></tr>
175<tr class="memitem:a638295d292bfdcf71899b57396703c80"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
176<tr class="memitem:a638295d292bfdcf71899b57396703c80"><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="_conv2d_test_impl_8cpp.xhtml#a638295d292bfdcf71899b57396703c80">CompareConvolution2dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;refWorkloadFactory)</td></tr>
177<tr class="separator:a638295d292bfdcf71899b57396703c80"><td class="memSeparator" colspan="2">&#160;</td></tr>
178<tr class="memitem:aa405363108e52032fb1e23c3f5a03a57"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;, typename B = armnn::ResolveType&lt;ArmnnBType&gt;&gt; </td></tr>
179<tr class="memitem:aa405363108e52032fb1e23c3f5a03a57"><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="_conv2d_test_impl_8cpp.xhtml#aa405363108e52032fb1e23c3f5a03a57">DepthwiseConvolution2dAsymmetricTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const boost::multi_array&lt; T, 4 &gt; &amp;input, const boost::multi_array&lt; T, 4 &gt; &amp;kernel, const boost::multi_array&lt; B, 1 &gt; &amp;bias, const boost::multi_array&lt; T, 4 &gt; &amp;outputExpected, float qScale, int32_t qOffset, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout, uint32_t padLeft=0, uint32_t padTop=0, uint32_t padRight=0, uint32_t padBottom=0, uint32_t strideX=1, uint32_t strideY=1)</td></tr>
180<tr class="separator:aa405363108e52032fb1e23c3f5a03a57"><td class="memSeparator" colspan="2">&#160;</td></tr>
181<tr class="memitem:a01eae690cbfa5359968f4b8ee13b8814"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
182<tr class="memitem:a01eae690cbfa5359968f4b8ee13b8814"><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="_conv2d_test_impl_8cpp.xhtml#a01eae690cbfa5359968f4b8ee13b8814">DepthwiseConvolution2dDepthMul1TestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
183<tr class="separator:a01eae690cbfa5359968f4b8ee13b8814"><td class="memSeparator" colspan="2">&#160;</td></tr>
184<tr class="memitem:ae3cc54b77789d10caeb5a438a0821ba0"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
185<tr class="memitem:ae3cc54b77789d10caeb5a438a0821ba0"><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="_conv2d_test_impl_8cpp.xhtml#ae3cc54b77789d10caeb5a438a0821ba0">DepthwiseConvolution2dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
186<tr class="separator:ae3cc54b77789d10caeb5a438a0821ba0"><td class="memSeparator" colspan="2">&#160;</td></tr>
187<tr class="memitem:a46e9706106f1b08c964d953154c66ad6"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;, typename B = armnn::ResolveType&lt;ArmnnBType&gt;&gt; </td></tr>
188<tr class="memitem:a46e9706106f1b08c964d953154c66ad6"><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="_conv2d_test_impl_8cpp.xhtml#a46e9706106f1b08c964d953154c66ad6">DepthwiseConvolution2dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const boost::multi_array&lt; T, 4 &gt; &amp;originalInput, const boost::multi_array&lt; T, 4 &gt; &amp;originalKernel, const boost::multi_array&lt; B, 1 &gt; &amp;bias, const boost::multi_array&lt; T, 4 &gt; &amp;originalOutputExpected, float qScale, int32_t qOffset, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout=<a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>, uint32_t padLeft=0, uint32_t padTop=0, uint32_t padRight=0, uint32_t padBottom=0, uint32_t strideX=1, uint32_t strideY=1, uint32_t dilationX=1, uint32_t dilationY=1)</td></tr>
189<tr class="separator:a46e9706106f1b08c964d953154c66ad6"><td class="memSeparator" colspan="2">&#160;</td></tr>
190<tr class="memitem:a952b4460c66365d89ebb3df940bbd9bd"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
191<tr class="memitem:a952b4460c66365d89ebb3df940bbd9bd"><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="_conv2d_test_impl_8cpp.xhtml#a952b4460c66365d89ebb3df940bbd9bd">DepthwiseConvolution2dAsymmetricTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
192<tr class="separator:a952b4460c66365d89ebb3df940bbd9bd"><td class="memSeparator" colspan="2">&#160;</td></tr>
193<tr class="memitem:a6271caa80dbf6fc82f97081d3d99d987"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
194<tr class="memitem:a6271caa80dbf6fc82f97081d3d99d987"><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="_conv2d_test_impl_8cpp.xhtml#a6271caa80dbf6fc82f97081d3d99d987">DepthwiseConvolution2dNhwcTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled)</td></tr>
195<tr class="separator:a6271caa80dbf6fc82f97081d3d99d987"><td class="memSeparator" colspan="2">&#160;</td></tr>
196<tr class="memitem:ac7af28eafb5b583057bca4471ce22328"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
197<tr class="memitem:ac7af28eafb5b583057bca4471ce22328"><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="_conv2d_test_impl_8cpp.xhtml#ac7af28eafb5b583057bca4471ce22328">SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, float qScale, int32_t qOffset, bool biasEnabled)</td></tr>
198<tr class="separator:ac7af28eafb5b583057bca4471ce22328"><td class="memSeparator" colspan="2">&#160;</td></tr>
199<tr class="memitem:a80ee4cde34185af792db65aa40cf5c98"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
200<tr class="memitem:a80ee4cde34185af792db65aa40cf5c98"><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="_conv2d_test_impl_8cpp.xhtml#a80ee4cde34185af792db65aa40cf5c98">DepthwiseConvolution2d3x3DilationTestCommon</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, const std::vector&lt; float &gt; &amp;inputNoQuantizedValues, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;inputTensorInfo, const std::vector&lt; float &gt; &amp;kernelNoQuantizedValues, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;kernelTensorInfo, const std::vector&lt; float &gt; &amp;outputExpectedNoQuantizedValues, <a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;outputTensorInfo, uint32_t dilationX, uint32_t dilationY, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout=<a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>, bool biasEnabled=<a class="el" href="_ref_layer_tests_8cpp.xhtml#af3b727ae5a13ff472892ab8bda2eb1b5">false</a>)</td></tr>
201<tr class="separator:a80ee4cde34185af792db65aa40cf5c98"><td class="memSeparator" colspan="2">&#160;</td></tr>
202<tr class="memitem:a1c3398bdb48e4ce4643a1eeaf3e054a3"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T &gt; </td></tr>
203<tr class="memitem:a1c3398bdb48e4ce4643a1eeaf3e054a3"><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="_conv2d_test_impl_8cpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3">DepthwiseConvolution2d3x3Dilation3x3Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
204<tr class="separator:a1c3398bdb48e4ce4643a1eeaf3e054a3"><td class="memSeparator" colspan="2">&#160;</td></tr>
205<tr class="memitem:acffa50ae3185e3e5302909f27e7e9a02"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T &gt; </td></tr>
206<tr class="memitem:acffa50ae3185e3e5302909f27e7e9a02"><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="_conv2d_test_impl_8cpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02">DepthwiseConvolution2d2x3x3Dilation3x3Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
207<tr class="separator:acffa50ae3185e3e5302909f27e7e9a02"><td class="memSeparator" colspan="2">&#160;</td></tr>
208<tr class="memitem:a0da6534b3a5d2f923dcd73553950129a"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T &gt; </td></tr>
209<tr class="memitem:a0da6534b3a5d2f923dcd73553950129a"><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="_conv2d_test_impl_8cpp.xhtml#a0da6534b3a5d2f923dcd73553950129a">DepthwiseConvolution2dMult4Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
210<tr class="separator:a0da6534b3a5d2f923dcd73553950129a"><td class="memSeparator" colspan="2">&#160;</td></tr>
211<tr class="memitem:aaed50a372a6b59b20e38469856a3ce6b"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T &gt; </td></tr>
212<tr class="memitem:aaed50a372a6b59b20e38469856a3ce6b"><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="_conv2d_test_impl_8cpp.xhtml#aaed50a372a6b59b20e38469856a3ce6b">DepthwiseConvolution2dMult2Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
213<tr class="separator:aaed50a372a6b59b20e38469856a3ce6b"><td class="memSeparator" colspan="2">&#160;</td></tr>
214<tr class="memitem:acac29a0b58c3c3f2928e0d7ee258c066"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
215<tr class="memitem:acac29a0b58c3c3f2928e0d7ee258c066"><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="_conv2d_test_impl_8cpp.xhtml#acac29a0b58c3c3f2928e0d7ee258c066">CompareDepthwiseConvolution2dTestImpl</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;refWorkloadFactory, const <a class="el" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> &amp;layout)</td></tr>
216<tr class="separator:acac29a0b58c3c3f2928e0d7ee258c066"><td class="memSeparator" colspan="2">&#160;</td></tr>
217<tr class="memitem:a964c2340d3764cc09df574364ff2633c"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a964c2340d3764cc09df574364ff2633c">Convolution2d3x3Dilation3x3Test&lt; armnn::DataType::BFloat16, armnn::DataType::BFloat16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
218<tr class="separator:a964c2340d3764cc09df574364ff2633c"><td class="memSeparator" colspan="2">&#160;</td></tr>
219<tr class="memitem:a7ea8f82c89483fdec102125b82a798c7"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a7ea8f82c89483fdec102125b82a798c7">Convolution2d3x3Dilation3x3Test&lt; armnn::DataType::Float32, armnn::DataType::Float32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
220<tr class="separator:a7ea8f82c89483fdec102125b82a798c7"><td class="memSeparator" colspan="2">&#160;</td></tr>
221<tr class="memitem:ac580208ebb11ac2d93076a5a7a346b9f"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#ac580208ebb11ac2d93076a5a7a346b9f">Convolution2d3x3Dilation3x3Test&lt; armnn::DataType::QAsymmU8, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
222<tr class="separator:ac580208ebb11ac2d93076a5a7a346b9f"><td class="memSeparator" colspan="2">&#160;</td></tr>
223<tr class="memitem:af84d6d89c899073318abbfa25292c36e"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#af84d6d89c899073318abbfa25292c36e">Convolution2d3x3Dilation3x3Test&lt; armnn::DataType::QSymmS16, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
224<tr class="separator:af84d6d89c899073318abbfa25292c36e"><td class="memSeparator" colspan="2">&#160;</td></tr>
225<tr class="memitem:ae4aeb75cd7f8051b6715ac315ae88254"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#ae4aeb75cd7f8051b6715ac315ae88254">Convolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::Float32, armnn::DataType::Float32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
226<tr class="separator:ae4aeb75cd7f8051b6715ac315ae88254"><td class="memSeparator" colspan="2">&#160;</td></tr>
227<tr class="memitem:a4885cb216d86099b0868c3b52fecb3e0"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a4885cb216d86099b0868c3b52fecb3e0">Convolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::BFloat16, armnn::DataType::BFloat16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
228<tr class="separator:a4885cb216d86099b0868c3b52fecb3e0"><td class="memSeparator" colspan="2">&#160;</td></tr>
229<tr class="memitem:aa2e414537fb1d51510cd7d1d3c85066b"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#aa2e414537fb1d51510cd7d1d3c85066b">Convolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::QAsymmU8, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
230<tr class="separator:aa2e414537fb1d51510cd7d1d3c85066b"><td class="memSeparator" colspan="2">&#160;</td></tr>
231<tr class="memitem:a48050c4e985c5741b51b55eb9961a19a"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a48050c4e985c5741b51b55eb9961a19a">Convolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::QSymmS16, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
232<tr class="separator:a48050c4e985c5741b51b55eb9961a19a"><td class="memSeparator" colspan="2">&#160;</td></tr>
233<tr class="memitem:a5a8681c1a9f05ad14b3a80b2524b2ea5"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a5a8681c1a9f05ad14b3a80b2524b2ea5">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test&lt; armnn::DataType::BFloat16, armnn::DataType::BFloat16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
234<tr class="separator:a5a8681c1a9f05ad14b3a80b2524b2ea5"><td class="memSeparator" colspan="2">&#160;</td></tr>
235<tr class="memitem:a72ba5d8a546cd3e8bf890058d74959d1"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a72ba5d8a546cd3e8bf890058d74959d1">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test&lt; armnn::DataType::Float32, armnn::DataType::Float32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
236<tr class="separator:a72ba5d8a546cd3e8bf890058d74959d1"><td class="memSeparator" colspan="2">&#160;</td></tr>
237<tr class="memitem:adfbd5fcca8b67b69f528fd1a270a1c53"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#adfbd5fcca8b67b69f528fd1a270a1c53">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test&lt; armnn::DataType::QAsymmU8, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
238<tr class="separator:adfbd5fcca8b67b69f528fd1a270a1c53"><td class="memSeparator" colspan="2">&#160;</td></tr>
239<tr class="memitem:a0ca68580fabbe96baccab2139bf8fec3"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a0ca68580fabbe96baccab2139bf8fec3">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test&lt; armnn::DataType::QSymmS16, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
240<tr class="separator:a0ca68580fabbe96baccab2139bf8fec3"><td class="memSeparator" colspan="2">&#160;</td></tr>
241<tr class="memitem:a003cb9146f0c41e02eedcd250546ba74"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a003cb9146f0c41e02eedcd250546ba74">DepthwiseConvolution2d3x3Dilation3x3Test&lt; armnn::DataType::BFloat16, armnn::DataType::BFloat16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
242<tr class="separator:a003cb9146f0c41e02eedcd250546ba74"><td class="memSeparator" colspan="2">&#160;</td></tr>
243<tr class="memitem:a5d3f9d15fbc0e3f43e100efb545e6ce6"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a5d3f9d15fbc0e3f43e100efb545e6ce6">DepthwiseConvolution2d3x3Dilation3x3Test&lt; armnn::DataType::Float32, armnn::DataType::Float32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
244<tr class="separator:a5d3f9d15fbc0e3f43e100efb545e6ce6"><td class="memSeparator" colspan="2">&#160;</td></tr>
245<tr class="memitem:a7703f4745f048b3a0b0c082b01d9715e"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a7703f4745f048b3a0b0c082b01d9715e">DepthwiseConvolution2d3x3Dilation3x3Test&lt; armnn::DataType::QAsymmU8, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
246<tr class="separator:a7703f4745f048b3a0b0c082b01d9715e"><td class="memSeparator" colspan="2">&#160;</td></tr>
247<tr class="memitem:ae2611d5cac758d2eebff6450315aa7df"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#ae2611d5cac758d2eebff6450315aa7df">DepthwiseConvolution2d3x3Dilation3x3Test&lt; armnn::DataType::QSymmS16, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
248<tr class="separator:ae2611d5cac758d2eebff6450315aa7df"><td class="memSeparator" colspan="2">&#160;</td></tr>
249<tr class="memitem:a0c016403b54cf7386462b18a01e49a60"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a0c016403b54cf7386462b18a01e49a60">DepthwiseConvolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::BFloat16, armnn::DataType::BFloat16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
250<tr class="separator:a0c016403b54cf7386462b18a01e49a60"><td class="memSeparator" colspan="2">&#160;</td></tr>
251<tr class="memitem:abfba475aaa254cb80fea6f6b9e2885ed"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#abfba475aaa254cb80fea6f6b9e2885ed">DepthwiseConvolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::Float32, armnn::DataType::Float32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
252<tr class="separator:abfba475aaa254cb80fea6f6b9e2885ed"><td class="memSeparator" colspan="2">&#160;</td></tr>
253<tr class="memitem:a7d1005e18161a898d383f302bda746ea"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a7d1005e18161a898d383f302bda746ea">DepthwiseConvolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::QAsymmU8, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
254<tr class="separator:a7d1005e18161a898d383f302bda746ea"><td class="memSeparator" colspan="2">&#160;</td></tr>
255<tr class="memitem:adc98546ccc8455972832038cf8a296c9"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#adc98546ccc8455972832038cf8a296c9">DepthwiseConvolution2d2x3x3Dilation3x3Test&lt; armnn::DataType::QSymmS16, armnn::DataType::Signed32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;, bool, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>)</td></tr>
256<tr class="separator:adc98546ccc8455972832038cf8a296c9"><td class="memSeparator" colspan="2">&#160;</td></tr>
257<tr class="memitem:a458125d04d00674f4bb30ef5c8d8e74f"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a458125d04d00674f4bb30ef5c8d8e74f">DepthwiseConvolution2dMult4Test&lt; armnn::DataType::BFloat16, armnn::DataType::BFloat16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
258<tr class="separator:a458125d04d00674f4bb30ef5c8d8e74f"><td class="memSeparator" colspan="2">&#160;</td></tr>
259<tr class="memitem:a52590a78e77f52f9be313967c35b870b"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a52590a78e77f52f9be313967c35b870b">DepthwiseConvolution2dMult4Test&lt; armnn::DataType::Float32, armnn::DataType::Float32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
260<tr class="separator:a52590a78e77f52f9be313967c35b870b"><td class="memSeparator" colspan="2">&#160;</td></tr>
261<tr class="memitem:aebd0b859b0bac0ebaf2812e7991f268d"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#aebd0b859b0bac0ebaf2812e7991f268d">DepthwiseConvolution2dMult2Test&lt; armnn::DataType::BFloat16, armnn::DataType::BFloat16 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
262<tr class="separator:aebd0b859b0bac0ebaf2812e7991f268d"><td class="memSeparator" colspan="2">&#160;</td></tr>
263<tr class="memitem:a3097119efa3acd563c309feec628b233"><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">armnn::ResolveType</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt;, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#a3097119efa3acd563c309feec628b233">DepthwiseConvolution2dMult2Test&lt; armnn::DataType::Float32, armnn::DataType::Float32 &gt;</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
264<tr class="separator:a3097119efa3acd563c309feec628b233"><td class="memSeparator" colspan="2">&#160;</td></tr>
265<tr class="memitem:afb5e7d86e241292d9cb899b960da54af"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; float, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#afb5e7d86e241292d9cb899b960da54af">SimpleConvolution2d3x5Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
266<tr class="separator:afb5e7d86e241292d9cb899b960da54af"><td class="memSeparator" colspan="2">&#160;</td></tr>
267<tr class="memitem:a8ffca1c4b38a68b10ba06f4f1416660f"><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="_conv2d_test_impl_8cpp.xhtml#a8ffca1c4b38a68b10ba06f4f1416660f">SimpleConvolution2d3x5Uint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
268<tr class="separator:a8ffca1c4b38a68b10ba06f4f1416660f"><td class="memSeparator" colspan="2">&#160;</td></tr>
269<tr class="memitem:acbe1a2adccd9e0aad14fc0ccb9266b0d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt; float, 4 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_conv2d_test_impl_8cpp.xhtml#acbe1a2adccd9e0aad14fc0ccb9266b0d">SimpleConvolution2d3x3Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
270<tr class="separator:acbe1a2adccd9e0aad14fc0ccb9266b0d"><td class="memSeparator" colspan="2">&#160;</td></tr>
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317<tr class="memitem:a21af5850bca4df2ea0315afb407e7900"><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="_conv2d_test_impl_8cpp.xhtml#a21af5850bca4df2ea0315afb407e7900">CompareDepthwiseConvolution2dUint8Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;refWorkloadFactory, const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</td></tr>
318<tr class="separator:a21af5850bca4df2ea0315afb407e7900"><td class="memSeparator" colspan="2">&#160;</td></tr>
319</table>
320<h2 class="groupheader">Function Documentation</h2>
321<a id="aa1f4ce02e0904dc8cf1b7f42bc34d346"></a>
322<h2 class="memtitle"><span class="permalink"><a href="#aa1f4ce02e0904dc8cf1b7f42bc34d346">&#9670;&nbsp;</a></span>ApplyBias()</h2>
323
324<div class="memitem">
325<div class="memproto">
326 <table class="memname">
327 <tr>
328 <td class="memname">void ApplyBias </td>
329 <td>(</td>
330 <td class="paramtype">std::vector&lt; T &gt; &amp;&#160;</td>
331 <td class="paramname"><em>v</em>, </td>
332 </tr>
333 <tr>
334 <td class="paramkey"></td>
335 <td></td>
336 <td class="paramtype">float&#160;</td>
337 <td class="paramname"><em>vScale</em>, </td>
338 </tr>
339 <tr>
340 <td class="paramkey"></td>
341 <td></td>
342 <td class="paramtype">int32_t&#160;</td>
343 <td class="paramname"><em>vOffset</em>, </td>
344 </tr>
345 <tr>
346 <td class="paramkey"></td>
347 <td></td>
348 <td class="paramtype">const std::vector&lt; B &gt; &amp;&#160;</td>
349 <td class="paramname"><em>bias</em>, </td>
350 </tr>
351 <tr>
352 <td class="paramkey"></td>
353 <td></td>
354 <td class="paramtype">float&#160;</td>
355 <td class="paramname"><em>bScale</em>, </td>
356 </tr>
357 <tr>
358 <td class="paramkey"></td>
359 <td></td>
360 <td class="paramtype">int32_t&#160;</td>
361 <td class="paramname"><em>bOffset</em>, </td>
362 </tr>
363 <tr>
364 <td class="paramkey"></td>
365 <td></td>
366 <td class="paramtype">uint32_t&#160;</td>
367 <td class="paramname"><em>w</em>, </td>
368 </tr>
369 <tr>
370 <td class="paramkey"></td>
371 <td></td>
372 <td class="paramtype">uint32_t&#160;</td>
373 <td class="paramname"><em>h</em>&#160;</td>
374 </tr>
375 <tr>
376 <td></td>
377 <td>)</td>
378 <td></td><td></td>
379 </tr>
380 </table>
381</div><div class="memdoc">
382
383<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00169">169</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
384
385<p class="reference">References <a class="el" href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">B</a>, and <a class="el" href="_quantize_helper_8hpp_source.xhtml#l00092">armnnUtils::SelectiveDequantize()</a>.</p>
386
387<p class="reference">Referenced by <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00460">Convolution1dTestImpl()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01381">DepthwiseConvolution2dAsymmetricTestImpl()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01518">DepthwiseConvolution2dDepthMul1TestImpl()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01671">DepthwiseConvolution2dTestImpl()</a>, and <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00201">SimpleConvolution2dTestImpl()</a>.</p>
388<div class="fragment"><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160;{</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; BOOST_ASSERT_MSG((armnn::IsQuantizedType&lt;T&gt;() &amp;&amp; vScale != 0.0f) || (!armnn::IsQuantizedType&lt;T&gt;()),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <span class="stringliteral">&quot;Invalid type and parameter combination.&quot;</span>);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; BOOST_ASSERT_MSG((armnn::IsQuantizedType&lt;B&gt;() &amp;&amp; bScale != 0.0f) || (!armnn::IsQuantizedType&lt;B&gt;()),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; <span class="stringliteral">&quot;Invalid type and parameter combination.&quot;</span>);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; <span class="comment">// Note we need to dequantize and re-quantize the image value and the bias.</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; <span class="keywordflow">for</span> (uint32_t i = 0; i &lt; bias.size(); ++i)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <span class="keywordtype">float</span> dBias = <a class="code" href="namespacearmnn_utils.xhtml#a5135dc1ce7a8aeb97623c1a92c5a3543">SelectiveDequantize</a>(bias[i], bScale, bOffset);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <span class="keywordflow">for</span> (uint32_t y = 0; y &lt; h; ++y)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <span class="keywordflow">for</span> (uint32_t x = 0; x &lt; w; ++x)</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; uint32_t offset = (i * h + y) * w + x;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; BOOST_ASSERT(offset &lt; v.size());</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; T&amp; outRef = v[offset];</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <span class="keywordtype">float</span> dOutput = <a class="code" href="namespacearmnn_utils.xhtml#a5135dc1ce7a8aeb97623c1a92c5a3543">SelectiveDequantize</a>(outRef, vScale, vOffset);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; outRef = SelectiveQuantize&lt;T&gt;(dOutput + dBias, vScale, vOffset);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; }</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; }</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160;}</div><div class="ttc" id="namespacearmnn_utils_xhtml_a5135dc1ce7a8aeb97623c1a92c5a3543"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a5135dc1ce7a8aeb97623c1a92c5a3543">armnnUtils::SelectiveDequantize</a></div><div class="ttdeci">float SelectiveDequantize(T value, float scale, int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_quantize_helper_8hpp_source.xhtml#l00092">QuantizeHelper.hpp:92</a></div></div>
389</div><!-- fragment -->
390</div>
391</div>
392<a id="a2b2c2f8f89d96932e62b95e7a22961d4"></a>
393<h2 class="memtitle"><span class="permalink"><a href="#a2b2c2f8f89d96932e62b95e7a22961d4">&#9670;&nbsp;</a></span>CompareConvolution2dTest()</h2>
394
395<div class="memitem">
396<div class="memproto">
397 <table class="memname">
398 <tr>
399 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float,4&gt; CompareConvolution2dTest </td>
400 <td>(</td>
401 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
402 <td class="paramname"><em>workloadFactory</em>, </td>
403 </tr>
404 <tr>
405 <td class="paramkey"></td>
406 <td></td>
407 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
408 <td class="paramname"><em>memoryManager</em>, </td>
409 </tr>
410 <tr>
411 <td class="paramkey"></td>
412 <td></td>
413 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
414 <td class="paramname"><em>refWorkloadFactory</em>&#160;</td>
415 </tr>
416 <tr>
417 <td></td>
418 <td>)</td>
419 <td></td><td></td>
420 </tr>
421 </table>
422</div><div class="memdoc">
423
424<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03184">3184</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
425<div class="fragment"><div class="line"><a name="l03188"></a><span class="lineno"> 3188</span>&#160;{</div><div class="line"><a name="l03189"></a><span class="lineno"> 3189</span>&#160; <span class="keywordflow">return</span> CompareConvolution2dTestImpl&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03190"></a><span class="lineno"> 3190</span>&#160; workloadFactory, memoryManager, refWorkloadFactory);</div><div class="line"><a name="l03191"></a><span class="lineno"> 3191</span>&#160;}</div></div><!-- fragment -->
426</div>
427</div>
428<a id="a638295d292bfdcf71899b57396703c80"></a>
429<h2 class="memtitle"><span class="permalink"><a href="#a638295d292bfdcf71899b57396703c80">&#9670;&nbsp;</a></span>CompareConvolution2dTestImpl()</h2>
430
431<div class="memitem">
432<div class="memproto">
433 <table class="memname">
434 <tr>
435 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T,4&gt; CompareConvolution2dTestImpl </td>
436 <td>(</td>
437 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
438 <td class="paramname"><em>workloadFactory</em>, </td>
439 </tr>
440 <tr>
441 <td class="paramkey"></td>
442 <td></td>
443 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
444 <td class="paramname"><em>memoryManager</em>, </td>
445 </tr>
446 <tr>
447 <td class="paramkey"></td>
448 <td></td>
449 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
450 <td class="paramname"><em>refWorkloadFactory</em>&#160;</td>
451 </tr>
452 <tr>
453 <td></td>
454 <td>)</td>
455 <td></td><td></td>
456 </tr>
457 </table>
458</div><div class="memdoc">
459
460<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01277">1277</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
461
462<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <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#l01159">IWorkloadFactory::CreateConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00177">Convolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00438">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00428">Convolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00422">Convolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00424">Convolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00426">Convolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00430">Convolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00432">Convolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00176">Convolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>, and <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
463<div class="fragment"><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>&#160;{</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 8;</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 5;</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>&#160;</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 3;</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</div><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; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = 2;</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = 3;</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padX = 1;</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padY = 1;</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>&#160;</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 2;</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>&#160;</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc;</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth};</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = {outputChannels};</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>&#160;</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>&#160; inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, ArmnnType);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>&#160; outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, ArmnnType);</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>&#160; kernelDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, kernelShape, ArmnnType);</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>&#160; biasDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasShape, ArmnnType);</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>&#160;</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T,4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>&#160;</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>&#160; <span class="keyword">auto</span> input = MakeRandomTensor&lt;T, 4&gt;(inputTensorInfo, 124908);</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>&#160; <span class="keyword">auto</span> kernel = MakeRandomTensor&lt;T, 4&gt;(kernelDesc, 891234);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>&#160; <span class="keyword">auto</span> bias = MakeRandomTensor&lt;T, 1&gt;(biasDesc, 1028);</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>&#160;</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>&#160; std::unique_ptr&lt;armnn::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="l01322"></a><span class="lineno"> 1322</span>&#160; std::unique_ptr&lt;armnn::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="l01323"></a><span class="lineno"> 1323</span>&#160;</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>&#160; <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>&#160;</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, &amp;bias[0]);</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>&#160;</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor;</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padX;</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padX;</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padY;</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padY;</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>&#160;</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>&#160;</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>&#160; <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> refData = data;</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>&#160; SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>&#160; SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>&#160;</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(refData, refInfo);</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>&#160;</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>&#160; outputHandleRef-&gt;Allocate();</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>&#160; inputHandleRef-&gt;Allocate();</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>&#160;</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>&#160;</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>&#160;</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>&#160;</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>&#160; workloadRef-&gt;PostAllocationConfigure();</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>&#160; workloadRef-&gt;Execute();</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>&#160;</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.outputExpected[0][0][0][0], outputHandleRef.get());</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; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div>
464<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div>
465<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::Convolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00177">WorkloadData.hpp:177</a></div></div>
466<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>
467<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div>
468<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>
469<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div>
470<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div>
471<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::Convolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00176">WorkloadData.hpp:176</a></div></div>
472<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
473<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>
474<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div>
475<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>
476<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div>
477<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
478<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
479<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>
480<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>
481<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div>
482<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2184995027cd2c9f9980206de9658855"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">armnn::IWorkloadFactory::CreateConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConvolution2d(const Convolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01159">WorkloadFactory.cpp:1159</a></div></div>
483<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>
484</div><!-- fragment -->
485</div>
486</div>
487<a id="a09705f5e38cfc0d5bccc64791eb4f231"></a>
488<h2 class="memtitle"><span class="permalink"><a href="#a09705f5e38cfc0d5bccc64791eb4f231">&#9670;&nbsp;</a></span>CompareDepthwiseConvolution2dFloatTest()</h2>
489
490<div class="memitem">
491<div class="memproto">
492 <table class="memname">
493 <tr>
494 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; CompareDepthwiseConvolution2dFloatTest </td>
495 <td>(</td>
496 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
497 <td class="paramname"><em>workloadFactory</em>, </td>
498 </tr>
499 <tr>
500 <td class="paramkey"></td>
501 <td></td>
502 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
503 <td class="paramname"><em>memoryManager</em>, </td>
504 </tr>
505 <tr>
506 <td class="paramkey"></td>
507 <td></td>
508 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
509 <td class="paramname"><em>refWorkloadFactory</em>, </td>
510 </tr>
511 <tr>
512 <td class="paramkey"></td>
513 <td></td>
514 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
515 <td class="paramname"><em>layout</em>&#160;</td>
516 </tr>
517 <tr>
518 <td></td>
519 <td>)</td>
520 <td></td><td></td>
521 </tr>
522 </table>
523</div><div class="memdoc">
524
525<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03424">3424</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
526<div class="fragment"><div class="line"><a name="l03429"></a><span class="lineno"> 3429</span>&#160;{</div><div class="line"><a name="l03430"></a><span class="lineno"> 3430</span>&#160; <span class="keywordflow">return</span> CompareDepthwiseConvolution2dTestImpl&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03431"></a><span class="lineno"> 3431</span>&#160; workloadFactory, memoryManager, refWorkloadFactory, layout);</div><div class="line"><a name="l03432"></a><span class="lineno"> 3432</span>&#160;}</div></div><!-- fragment -->
527</div>
528</div>
529<a id="acac29a0b58c3c3f2928e0d7ee258c066"></a>
530<h2 class="memtitle"><span class="permalink"><a href="#acac29a0b58c3c3f2928e0d7ee258c066">&#9670;&nbsp;</a></span>CompareDepthwiseConvolution2dTestImpl()</h2>
531
532<div class="memitem">
533<div class="memproto">
534 <table class="memname">
535 <tr>
536 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; CompareDepthwiseConvolution2dTestImpl </td>
537 <td>(</td>
538 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
539 <td class="paramname"><em>workloadFactory</em>, </td>
540 </tr>
541 <tr>
542 <td class="paramkey"></td>
543 <td></td>
544 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
545 <td class="paramname"><em>memoryManager</em>, </td>
546 </tr>
547 <tr>
548 <td class="paramkey"></td>
549 <td></td>
550 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
551 <td class="paramname"><em>refWorkloadFactory</em>, </td>
552 </tr>
553 <tr>
554 <td class="paramkey"></td>
555 <td></td>
556 <td class="paramtype">const <a class="el" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> &amp;&#160;</td>
557 <td class="paramname"><em>layout</em>&#160;</td>
558 </tr>
559 <tr>
560 <td></td>
561 <td>)</td>
562 <td></td><td></td>
563 </tr>
564 </table>
565</div><div class="memdoc">
566
567<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02669">2669</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
568
569<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <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#l01177">IWorkloadFactory::CreateDepthwiseConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_workload_data_8cpp_source.xhtml#l00025">armnn::GetBiasDataType()</a>, <a class="el" href="_data_layout_indexed_8hpp_source.xhtml#l00022">DataLayoutIndexed::GetDataLayout()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00192">DepthwiseConvolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00490">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00492">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00480">DepthwiseConvolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00474">DepthwiseConvolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00476">DepthwiseConvolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00478">DepthwiseConvolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00482">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00484">DepthwiseConvolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00191">DepthwiseConvolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</a>, and <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p>
570<div class="fragment"><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>&#160;{</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 8;</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 5;</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; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 3;</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelMultiplier = 1;</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>&#160;</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = 2;</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = 3;</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padX = 1;</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padY = 1;</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>&#160;</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels * channelMultiplier;</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;</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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc;</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc;</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>&#160;</div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>&#160;</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>&#160; std::vector&lt;unsigned int&gt; inputShape;</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>&#160; std::vector&lt;unsigned int&gt; outputShape;</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>&#160; std::vector&lt;unsigned int&gt; kernelShape{ channelMultiplier, inputChannels, kernelHeight, kernelWidth };</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>&#160; std::vector&lt;unsigned int&gt; biasShape{ outputChannels };</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>&#160; <span class="keywordflow">switch</span> (layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">GetDataLayout</a>())</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span>&#160; {</div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>:</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>&#160; inputShape = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>&#160; outputShape = { outputNum, outputChannels, outputHeight, outputWidth };</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout ::NHWC</a>:</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>&#160; inputShape = { inputNum, inputHeight, inputWidth, inputChannels };</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>&#160; outputShape = { outputNum, outputHeight, outputWidth, outputChannels };</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>&#160; <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>(<span class="stringliteral">&quot;unknown data layout [&quot;</span></div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>&#160; + std::to_string(static_cast&lt;int&gt;(layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">GetDataLayout</a>())) + <span class="stringliteral">&quot;]&quot;</span>);</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>&#160; }</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>&#160;</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>&#160; <span class="keywordtype">float</span> inputsQScale = armnn::IsQuantizedType&lt;T&gt;() ? 1.0f : 0;</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>&#160; <span class="keywordtype">float</span> outputQScale = armnn::IsQuantizedType&lt;T&gt;() ? 2.0f : 0;</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>&#160; int32_t qOffset = 0;</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>&#160;</div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>&#160; inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape.data(), ArmnnType, inputsQScale, qOffset);</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>&#160; outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape.data(), ArmnnType, outputQScale, qOffset);</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>&#160; kernelDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, kernelShape.data(), ArmnnType, inputsQScale, qOffset);</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>&#160; biasDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>&#160; 1, biasShape.data(), <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">armnn::GetBiasDataType</a>(ArmnnType), inputsQScale, qOffset);</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; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> ret(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="keyword">auto</span> input = MakeRandomTensor&lt;T, 4&gt;(inputTensorInfo, 124908, 0.0f, 255.0f);</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>&#160; <span class="keyword">auto</span> kernel = MakeRandomTensor&lt;T, 4&gt;(kernelDesc, 891234, 0.0f, 255.0f);</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>&#160; <span class="keyword">auto</span> bias = MakeRandomTensor&lt;typename FullyConnectedBiasTypeForInputType&lt;T&gt;::Type, 1&gt;(</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>&#160; biasDesc, 1028, 0.0f, 255.0f);</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>&#160;</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>&#160; std::unique_ptr&lt;armnn::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="l02737"></a><span class="lineno"> 2737</span>&#160; std::unique_ptr&lt;armnn::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="l02738"></a><span class="lineno"> 2738</span>&#160;</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</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="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, &amp;bias[0]);</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>&#160;</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.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; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor;</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padX;</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padX;</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padY;</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padY;</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">GetDataLayout</a>();</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>&#160;</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>&#160;</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> refData = data;</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>&#160; SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>&#160; SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span>&#160;</div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(refData, refInfo);</div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span>&#160;</div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>&#160; outputHandleRef-&gt;Allocate();</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>&#160; inputHandleRef-&gt;Allocate();</div><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; inputHandle-&gt;Allocate();</div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>&#160;</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span>&#160;</div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>&#160;</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>&#160; workloadRef-&gt;PostAllocationConfigure();</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>&#160; workloadRef-&gt;Execute();</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span>&#160;</div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.outputExpected[0][0][0][0], outputHandleRef.get());</div><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> ret;</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div>
571<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div>
572<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div>
573<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>
574<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::DepthwiseConvolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00192">WorkloadData.hpp:192</a></div></div>
575<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div>
576<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>
577<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::DepthwiseConvolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00191">WorkloadData.hpp:191</a></div></div>
578<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</a></div></div>
579<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div>
580<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
581<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a7d8b3d755b6ca8f5533657969efb06c4"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">armnnUtils::DataLayoutIndexed::GetDataLayout</a></div><div class="ttdeci">armnn::DataLayout GetDataLayout() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00022">DataLayoutIndexed.hpp:22</a></div></div>
582<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>
583<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
584<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>
585<div class="ttc" id="namespacearmnn_xhtml_a872803f5667392efc3c8e5607bd453ad"><div class="ttname"><a href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">armnn::GetBiasDataType</a></div><div class="ttdeci">DataType GetBiasDataType(DataType inputDataType)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00025">WorkloadData.cpp:25</a></div></div>
586<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
587<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div>
588<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
589<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>
590<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>
591<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
592<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_accb9759dfd2880efe0f8d2705ddee448"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">armnn::IWorkloadFactory::CreateDepthwiseConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01177">WorkloadFactory.cpp:1177</a></div></div>
593<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00183">WorkloadData.hpp:183</a></div></div>
594<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
595<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>
596<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div>
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601<h2 class="memtitle"><span class="permalink"><a href="#a21af5850bca4df2ea0315afb407e7900">&#9670;&nbsp;</a></span>CompareDepthwiseConvolution2dUint8Test()</h2>
602
603<div class="memitem">
604<div class="memproto">
605 <table class="memname">
606 <tr>
607 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; CompareDepthwiseConvolution2dUint8Test </td>
608 <td>(</td>
609 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
610 <td class="paramname"><em>workloadFactory</em>, </td>
611 </tr>
612 <tr>
613 <td class="paramkey"></td>
614 <td></td>
615 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
616 <td class="paramname"><em>memoryManager</em>, </td>
617 </tr>
618 <tr>
619 <td class="paramkey"></td>
620 <td></td>
621 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
622 <td class="paramname"><em>refWorkloadFactory</em>, </td>
623 </tr>
624 <tr>
625 <td class="paramkey"></td>
626 <td></td>
627 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
628 <td class="paramname"><em>layout</em>&#160;</td>
629 </tr>
630 <tr>
631 <td></td>
632 <td>)</td>
633 <td></td><td></td>
634 </tr>
635 </table>
636</div><div class="memdoc">
637
638<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03434">3434</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
639<div class="fragment"><div class="line"><a name="l03439"></a><span class="lineno"> 3439</span>&#160;{</div><div class="line"><a name="l03440"></a><span class="lineno"> 3440</span>&#160; <span class="keywordflow">return</span> CompareDepthwiseConvolution2dTestImpl&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l03441"></a><span class="lineno"> 3441</span>&#160; workloadFactory, memoryManager, refWorkloadFactory, layout);</div><div class="line"><a name="l03442"></a><span class="lineno"> 3442</span>&#160;}</div></div><!-- fragment -->
640</div>
641</div>
642<a id="ac7fac5767dabd650d3d8829572717064"></a>
643<h2 class="memtitle"><span class="permalink"><a href="#ac7fac5767dabd650d3d8829572717064">&#9670;&nbsp;</a></span>Convolution1dTest()</h2>
644
645<div class="memitem">
646<div class="memproto">
647 <table class="memname">
648 <tr>
649 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Convolution1dTest </td>
650 <td>(</td>
651 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
652 <td class="paramname"><em>workloadFactory</em>, </td>
653 </tr>
654 <tr>
655 <td class="paramkey"></td>
656 <td></td>
657 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
658 <td class="paramname"><em>memoryManager</em>, </td>
659 </tr>
660 <tr>
661 <td class="paramkey"></td>
662 <td></td>
663 <td class="paramtype">bool&#160;</td>
664 <td class="paramname"><em>biasEnabled</em>&#160;</td>
665 </tr>
666 <tr>
667 <td></td>
668 <td>)</td>
669 <td></td><td></td>
670 </tr>
671 </table>
672</div><div class="memdoc">
673
674<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03074">3074</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
675<div class="fragment"><div class="line"><a name="l03078"></a><span class="lineno"> 3078</span>&#160;{</div><div class="line"><a name="l03079"></a><span class="lineno"> 3079</span>&#160; <span class="keywordflow">return</span> Convolution1dTestImpl&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span>&#160; workloadFactory, memoryManager, 0.0f, 0, biasEnabled);</div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span>&#160;}</div></div><!-- fragment -->
676</div>
677</div>
678<a id="af541f19e3d1ad345cc9208fc2d2e7b19"></a>
679<h2 class="memtitle"><span class="permalink"><a href="#af541f19e3d1ad345cc9208fc2d2e7b19">&#9670;&nbsp;</a></span>Convolution1dTestImpl()</h2>
680
681<div class="memitem">
682<div class="memproto">
683 <table class="memname">
684 <tr>
685 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T,4&gt; Convolution1dTestImpl </td>
686 <td>(</td>
687 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
688 <td class="paramname"><em>workloadFactory</em>, </td>
689 </tr>
690 <tr>
691 <td class="paramkey"></td>
692 <td></td>
693 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
694 <td class="paramname"><em>memoryManager</em>, </td>
695 </tr>
696 <tr>
697 <td class="paramkey"></td>
698 <td></td>
699 <td class="paramtype">float&#160;</td>
700 <td class="paramname"><em>qScale</em>, </td>
701 </tr>
702 <tr>
703 <td class="paramkey"></td>
704 <td></td>
705 <td class="paramtype">int32_t&#160;</td>
706 <td class="paramname"><em>qOffset</em>, </td>
707 </tr>
708 <tr>
709 <td class="paramkey"></td>
710 <td></td>
711 <td class="paramtype">bool&#160;</td>
712 <td class="paramname"><em>biasEnabled</em>&#160;</td>
713 </tr>
714 <tr>
715 <td></td>
716 <td>)</td>
717 <td></td><td></td>
718 </tr>
719 </table>
720</div><div class="memdoc">
721
722<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00460">460</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
723
724<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00169">ApplyBias()</a>, <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#l01159">IWorkloadFactory::CreateConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00177">Convolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00438">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00428">Convolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00422">Convolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00424">Convolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00426">Convolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00430">Convolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00432">Convolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00176">Convolution2dQueueDescriptor::m_Weight</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="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
725<div class="fragment"><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160;{</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; <span class="keyword">using</span> <a class="code" href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">B</a> = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType&lt;ArmnnBType&gt;</a>;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; <span class="comment">// Until we have a specialist 1D convolution layer, we can fake one using</span></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; <span class="comment">// 2D convolution with the final dimension set to 1.</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; <span class="comment">// I don&#39;t anticipate this being particularly slow, given that convolution is implemented</span></div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; <span class="comment">// as a matrix multiplication, at which point dimension doesn&#39;t matter.</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160;</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 1;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 2;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5; <span class="comment">// The 1D size (could view as &#39;width&#39; or &#39;height&#39;).</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelSize = 3;</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padSize = 2;</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride = 1;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 7; <span class="comment">// (inputSize + 2 * padSize - kernelSize + 1) / stride.</span></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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({batchSize, inputChannels, inputSize, 1}, ArmnnType);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({batchSize, outputChannels, outputSize, 1}, ArmnnType);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelInfo({outputChannels, inputChannels, kernelSize, 1}, ArmnnType);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({outputChannels}, ArmnnBType);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</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; inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160; inputInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; outputInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; outputInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; kernelInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; kernelInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale());</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; biasInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; }</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; std::vector&lt;T&gt; inputData = QuantizedVector&lt;T&gt;(</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; 5.0f, -2.0f, 2.5f, 0.0f, 1.0f,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; -3.0f, 3.2f, 5.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160; },</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; inputInfo.GetQuantizationScale(),</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; inputInfo.GetQuantizationOffset());</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; std::vector&lt;T&gt; kernelData = QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; {</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; 1.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; 0.0f, 2.0f, -1.5f,</div><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; 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160; 0.2f, 0.2f, 0.2f,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; 0.5f, 0.0f, 0.5f,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; 0.0f, -1.0f, 0.0f</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160; },</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; kernelInfo.GetQuantizationScale(),</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; kernelInfo.GetQuantizationOffset());</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160;</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; std::vector&lt;B&gt; biasData =</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; QuantizedVector&lt;B&gt;({ 1.0f, 0.0f, 0.0f }, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset());</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160; std::vector&lt;T&gt; outputData = QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; {</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; 4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160; -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; 2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; },</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; outputInfo.GetQuantizationScale(),</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; outputInfo.GetQuantizationOffset());</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; <span class="comment">// Optionally apply bias to output image.</span></div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(),</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(),</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160; 1, outputSize);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; }</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputInfo);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputInfo);</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>&#160;</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>&#160; <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelInfo);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasInfo);</div><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="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, kernelData.data());</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, biasData.data());</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160; AddInputToWorkload(data, info, inputInfo, inputHandle.get());</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; AddOutputToWorkload(data, info, outputInfo, outputHandle.get());</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160;</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor;</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = stride;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padSize;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padSize;</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>&#160;</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData.data());</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>&#160;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>&#160;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>&#160; <span class="comment">// Output</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T,4&gt;</a> ret(outputInfo);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputInfo, outputData);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div>
726<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div>
727<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::Convolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00177">WorkloadData.hpp:177</a></div></div>
728<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa1f4ce02e0904dc8cf1b7f42bc34d346"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a></div><div class="ttdeci">void ApplyBias(std::vector&lt; T &gt; &amp;v, float vScale, int32_t vOffset, const std::vector&lt; B &gt; &amp;bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00169">Conv2dTestImpl.cpp:169</a></div></div>
729<div class="ttc" id="_inference_test_image_8hpp_xhtml_a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571"><div class="ttname"><a href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">ImageChannel::B</a></div></div>
730<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>
731<div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl&lt; DT &gt;::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00073">ResolveType.hpp:73</a></div></div>
732<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div>
733<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>
734<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div>
735<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div>
736<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::Convolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00176">WorkloadData.hpp:176</a></div></div>
737<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>
738<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
739<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>
740<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div>
741<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>
742<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div>
743<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
744<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
745<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>
746<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>
747<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div>
748<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2184995027cd2c9f9980206de9658855"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">armnn::IWorkloadFactory::CreateConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConvolution2d(const Convolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01159">WorkloadFactory.cpp:1159</a></div></div>
749<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>
750</div><!-- fragment -->
751</div>
752</div>
753<a id="a40bc412ed2a6d2f764655070c02c036b"></a>
754<h2 class="memtitle"><span class="permalink"><a href="#a40bc412ed2a6d2f764655070c02c036b">&#9670;&nbsp;</a></span>Convolution1dUint8Test()</h2>
755
756<div class="memitem">
757<div class="memproto">
758 <table class="memname">
759 <tr>
760 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Convolution1dUint8Test </td>
761 <td>(</td>
762 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
763 <td class="paramname"><em>workloadFactory</em>, </td>
764 </tr>
765 <tr>
766 <td class="paramkey"></td>
767 <td></td>
768 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
769 <td class="paramname"><em>memoryManager</em>, </td>
770 </tr>
771 <tr>
772 <td class="paramkey"></td>
773 <td></td>
774 <td class="paramtype">bool&#160;</td>
775 <td class="paramname"><em>biasEnabled</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="_conv2d_test_impl_8cpp_source.xhtml#l03083">3083</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
786<div class="fragment"><div class="line"><a name="l03087"></a><span class="lineno"> 3087</span>&#160;{</div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span>&#160; <span class="keywordflow">return</span> Convolution1dTestImpl&lt;armnn::DataType::QAsymmU8, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span>&#160; workloadFactory, memoryManager, 0.1f, 128, biasEnabled);</div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span>&#160;}</div></div><!-- fragment -->
787</div>
788</div>
789<a id="acf553288e3b5060768fb91e064993678"></a>
790<h2 class="memtitle"><span class="permalink"><a href="#acf553288e3b5060768fb91e064993678">&#9670;&nbsp;</a></span>Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test()</h2>
791
792<div class="memitem">
793<div class="memproto">
794 <table class="memname">
795 <tr>
796 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test </td>
797 <td>(</td>
798 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
799 <td class="paramname"><em>workloadFactory</em>, </td>
800 </tr>
801 <tr>
802 <td class="paramkey"></td>
803 <td></td>
804 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
805 <td class="paramname"><em>memoryManager</em>, </td>
806 </tr>
807 <tr>
808 <td class="paramkey"></td>
809 <td></td>
810 <td class="paramtype">bool&#160;</td>
811 <td class="paramname"><em>biasEnabled</em>, </td>
812 </tr>
813 <tr>
814 <td class="paramkey"></td>
815 <td></td>
816 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
817 <td class="paramname"><em>layout</em>&#160;</td>
818 </tr>
819 <tr>
820 <td></td>
821 <td>)</td>
822 <td></td><td></td>
823 </tr>
824 </table>
825</div><div class="memdoc">
826
827<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01210">1210</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
828<div class="fragment"><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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 1, 10, 10}, ArmnnType);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>&#160; std::vector&lt;float&gt; inputNoQuantizedValues =</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>&#160; {</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>&#160; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>&#160; };</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>&#160;</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 2, 2}, ArmnnType);</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>&#160; std::vector&lt;float&gt; kernelNoQuantizedValues =</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>&#160; {</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>&#160; 1, 2,</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>&#160; 3, 4</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>&#160; };</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">// Since the dilation rate is 2 this will dilate the kernel to be like 3x3: d(K-1)+1 --&gt; 2 x (2-1) + 1 = 3,</span></div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>&#160; <span class="comment">// therefore the output will be 4x4: (I − K + 2P)/S +1 =&gt; trunc ( (10 - 3 + 2x2 ) / 3 + 1 )</span></div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>&#160; <span class="comment">// where, dilation size = d = 2; kernel size = K = 2; input size = I = 10; padding size = P = 2; stride = S = 3</span></div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>&#160; std::vector&lt;float&gt; outputExpectedNoQuantizedValues =</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; 4, 7, 7, 3,</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>&#160; 6, 10, 10, 4,</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>&#160; 6, 10, 10, 4,</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>&#160; 2, 3, 3, 1</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>&#160; };</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>&#160; uint32_t padLeft = 1;</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>&#160; uint32_t padTop = 1;</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>&#160; uint32_t padRight = 1;</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>&#160; uint32_t padBottom = 1;</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>&#160;</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>&#160; <span class="keywordflow">return</span> Convolution2d3x3DilationTestCommon&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>&#160; workloadFactory,</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>&#160; memoryManager,</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>&#160; inputNoQuantizedValues,</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>&#160; inputTensorInfo,</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>&#160; kernelNoQuantizedValues,</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>&#160; kernelTensorInfo,</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>&#160; outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>&#160; 2,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>&#160; 2,</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>&#160; layout,</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>&#160; padLeft,</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>&#160; padTop,</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>&#160; padRight,</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>&#160; padBottom,</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>&#160; 3,</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>&#160; 3,</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>&#160; biasEnabled</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;}</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>
829</div><!-- fragment -->
830</div>
831</div>
832<a id="a5a8681c1a9f05ad14b3a80b2524b2ea5"></a>
833<h2 class="memtitle"><span class="permalink"><a href="#a5a8681c1a9f05ad14b3a80b2524b2ea5">&#9670;&nbsp;</a></span>Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test< armnn::DataType::BFloat16, armnn::DataType::BFloat16 >()</h2>
834
835<div class="memitem">
836<div class="memproto">
837 <table class="memname">
838 <tr>
839 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acf553288e3b5060768fb91e064993678">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt; </td>
840 <td>(</td>
841 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
842 <td class="paramname"><em>workloadFactory</em>, </td>
843 </tr>
844 <tr>
845 <td class="paramkey"></td>
846 <td></td>
847 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
848 <td class="paramname"><em>memoryManager</em>, </td>
849 </tr>
850 <tr>
851 <td class="paramkey"></td>
852 <td></td>
853 <td class="paramtype">bool&#160;</td>
854 <td class="paramname"><em>biasEnabled</em>, </td>
855 </tr>
856 <tr>
857 <td class="paramkey"></td>
858 <td></td>
859 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
860 <td class="paramname"><em>layout</em>&#160;</td>
861 </tr>
862 <tr>
863 <td></td>
864 <td>)</td>
865 <td></td><td></td>
866 </tr>
867 </table>
868</div><div class="memdoc">
869
870</div>
871</div>
872<a id="a72ba5d8a546cd3e8bf890058d74959d1"></a>
873<h2 class="memtitle"><span class="permalink"><a href="#a72ba5d8a546cd3e8bf890058d74959d1">&#9670;&nbsp;</a></span>Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test< armnn::DataType::Float32, armnn::DataType::Float32 >()</h2>
874
875<div class="memitem">
876<div class="memproto">
877 <table class="memname">
878 <tr>
879 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acf553288e3b5060768fb91e064993678">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt; </td>
880 <td>(</td>
881 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
882 <td class="paramname"><em>workloadFactory</em>, </td>
883 </tr>
884 <tr>
885 <td class="paramkey"></td>
886 <td></td>
887 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
888 <td class="paramname"><em>memoryManager</em>, </td>
889 </tr>
890 <tr>
891 <td class="paramkey"></td>
892 <td></td>
893 <td class="paramtype">bool&#160;</td>
894 <td class="paramname"><em>biasEnabled</em>, </td>
895 </tr>
896 <tr>
897 <td class="paramkey"></td>
898 <td></td>
899 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
900 <td class="paramname"><em>layout</em>&#160;</td>
901 </tr>
902 <tr>
903 <td></td>
904 <td>)</td>
905 <td></td><td></td>
906 </tr>
907 </table>
908</div><div class="memdoc">
909
910</div>
911</div>
912<a id="adfbd5fcca8b67b69f528fd1a270a1c53"></a>
913<h2 class="memtitle"><span class="permalink"><a href="#adfbd5fcca8b67b69f528fd1a270a1c53">&#9670;&nbsp;</a></span>Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test< armnn::DataType::QAsymmU8, armnn::DataType::Signed32 >()</h2>
914
915<div class="memitem">
916<div class="memproto">
917 <table class="memname">
918 <tr>
919 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acf553288e3b5060768fb91e064993678">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
920 <td>(</td>
921 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
922 <td class="paramname"><em>workloadFactory</em>, </td>
923 </tr>
924 <tr>
925 <td class="paramkey"></td>
926 <td></td>
927 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
928 <td class="paramname"><em>memoryManager</em>, </td>
929 </tr>
930 <tr>
931 <td class="paramkey"></td>
932 <td></td>
933 <td class="paramtype">bool&#160;</td>
934 <td class="paramname"><em>biasEnabled</em>, </td>
935 </tr>
936 <tr>
937 <td class="paramkey"></td>
938 <td></td>
939 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
940 <td class="paramname"><em>layout</em>&#160;</td>
941 </tr>
942 <tr>
943 <td></td>
944 <td>)</td>
945 <td></td><td></td>
946 </tr>
947 </table>
948</div><div class="memdoc">
949
950</div>
951</div>
952<a id="a0ca68580fabbe96baccab2139bf8fec3"></a>
953<h2 class="memtitle"><span class="permalink"><a href="#a0ca68580fabbe96baccab2139bf8fec3">&#9670;&nbsp;</a></span>Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test< armnn::DataType::QSymmS16, armnn::DataType::Signed32 >()</h2>
954
955<div class="memitem">
956<div class="memproto">
957 <table class="memname">
958 <tr>
959 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acf553288e3b5060768fb91e064993678">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
960 <td>(</td>
961 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
962 <td class="paramname"><em>workloadFactory</em>, </td>
963 </tr>
964 <tr>
965 <td class="paramkey"></td>
966 <td></td>
967 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
968 <td class="paramname"><em>memoryManager</em>, </td>
969 </tr>
970 <tr>
971 <td class="paramkey"></td>
972 <td></td>
973 <td class="paramtype">bool&#160;</td>
974 <td class="paramname"><em>biasEnabled</em>, </td>
975 </tr>
976 <tr>
977 <td class="paramkey"></td>
978 <td></td>
979 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
980 <td class="paramname"><em>layout</em>&#160;</td>
981 </tr>
982 <tr>
983 <td></td>
984 <td>)</td>
985 <td></td><td></td>
986 </tr>
987 </table>
988</div><div class="memdoc">
989
990</div>
991</div>
992<a id="a99ef3f48cbd057e0169bc80dc77331ef"></a>
993<h2 class="memtitle"><span class="permalink"><a href="#a99ef3f48cbd057e0169bc80dc77331ef">&#9670;&nbsp;</a></span>Convolution2d2x3x3Dilation3x3Test()</h2>
994
995<div class="memitem">
996<div class="memproto">
997 <table class="memname">
998 <tr>
999 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Convolution2d2x3x3Dilation3x3Test </td>
1000 <td>(</td>
1001 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1002 <td class="paramname"><em>workloadFactory</em>, </td>
1003 </tr>
1004 <tr>
1005 <td class="paramkey"></td>
1006 <td></td>
1007 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1008 <td class="paramname"><em>memoryManager</em>, </td>
1009 </tr>
1010 <tr>
1011 <td class="paramkey"></td>
1012 <td></td>
1013 <td class="paramtype">bool&#160;</td>
1014 <td class="paramname"><em>biasEnabled</em>, </td>
1015 </tr>
1016 <tr>
1017 <td class="paramkey"></td>
1018 <td></td>
1019 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1020 <td class="paramname"><em>layout</em>&#160;</td>
1021 </tr>
1022 <tr>
1023 <td></td>
1024 <td>)</td>
1025 <td></td><td></td>
1026 </tr>
1027 </table>
1028</div><div class="memdoc">
1029
1030<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01139">1139</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1031<div class="fragment"><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>&#160;{</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 10, 10}, ArmnnType);</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>&#160; std::vector&lt;float&gt; inputNoQuantizedValues =</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>&#160; {</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</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; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>&#160; };</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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>&#160; std::vector&lt;float&gt; kernelNoQuantizedValues =</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>&#160; {</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>&#160; 1, 2, 3,</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>&#160; 4, 5, 6,</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>&#160; 7, 8, 9,</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>&#160;</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>&#160; 1, 2, 3,</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>&#160; 4, 5, 6,</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>&#160; 7, 8, 9</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;</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>&#160; <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>&#160; <span class="comment">// therefore the output will be 4x4: (I−K+2P)/S +1 =&gt; (10-7 +0)/1 +1</span></div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>&#160; std::vector&lt;float&gt; outputExpectedNoQuantizedValues =</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; 12., 10., 10., 10.,</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>&#160; 12., 10., 10., 10.,</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>&#160; 12., 10., 10., 10.,</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>&#160; 6., 4., 4., 4.</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>&#160; };</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="keywordflow">return</span> Convolution2d3x3DilationTestCommon&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>&#160; workloadFactory,</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>&#160; memoryManager,</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>&#160; inputNoQuantizedValues,</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>&#160; inputTensorInfo,</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>&#160; kernelNoQuantizedValues,</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>&#160; kernelTensorInfo,</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>&#160; outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>&#160; 3,</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>&#160; 3,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>&#160; layout,</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>&#160; biasEnabled);</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</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>
1032</div><!-- fragment -->
1033</div>
1034</div>
1035<a id="a4885cb216d86099b0868c3b52fecb3e0"></a>
1036<h2 class="memtitle"><span class="permalink"><a href="#a4885cb216d86099b0868c3b52fecb3e0">&#9670;&nbsp;</a></span>Convolution2d2x3x3Dilation3x3Test< armnn::DataType::BFloat16, armnn::DataType::BFloat16 >()</h2>
1037
1038<div class="memitem">
1039<div class="memproto">
1040 <table class="memname">
1041 <tr>
1042 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef">Convolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt; </td>
1043 <td>(</td>
1044 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1045 <td class="paramname">, </td>
1046 </tr>
1047 <tr>
1048 <td class="paramkey"></td>
1049 <td></td>
1050 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1051 <td class="paramname">, </td>
1052 </tr>
1053 <tr>
1054 <td class="paramkey"></td>
1055 <td></td>
1056 <td class="paramtype">bool&#160;</td>
1057 <td class="paramname">, </td>
1058 </tr>
1059 <tr>
1060 <td class="paramkey"></td>
1061 <td></td>
1062 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1063 <td class="paramname">&#160;</td>
1064 </tr>
1065 <tr>
1066 <td></td>
1067 <td>)</td>
1068 <td></td><td></td>
1069 </tr>
1070 </table>
1071</div><div class="memdoc">
1072
1073</div>
1074</div>
1075<a id="ae4aeb75cd7f8051b6715ac315ae88254"></a>
1076<h2 class="memtitle"><span class="permalink"><a href="#ae4aeb75cd7f8051b6715ac315ae88254">&#9670;&nbsp;</a></span>Convolution2d2x3x3Dilation3x3Test< armnn::DataType::Float32, armnn::DataType::Float32 >()</h2>
1077
1078<div class="memitem">
1079<div class="memproto">
1080 <table class="memname">
1081 <tr>
1082 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef">Convolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt; </td>
1083 <td>(</td>
1084 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1085 <td class="paramname">, </td>
1086 </tr>
1087 <tr>
1088 <td class="paramkey"></td>
1089 <td></td>
1090 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1091 <td class="paramname">, </td>
1092 </tr>
1093 <tr>
1094 <td class="paramkey"></td>
1095 <td></td>
1096 <td class="paramtype">bool&#160;</td>
1097 <td class="paramname">, </td>
1098 </tr>
1099 <tr>
1100 <td class="paramkey"></td>
1101 <td></td>
1102 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1103 <td class="paramname">&#160;</td>
1104 </tr>
1105 <tr>
1106 <td></td>
1107 <td>)</td>
1108 <td></td><td></td>
1109 </tr>
1110 </table>
1111</div><div class="memdoc">
1112
1113</div>
1114</div>
1115<a id="aa2e414537fb1d51510cd7d1d3c85066b"></a>
1116<h2 class="memtitle"><span class="permalink"><a href="#aa2e414537fb1d51510cd7d1d3c85066b">&#9670;&nbsp;</a></span>Convolution2d2x3x3Dilation3x3Test< armnn::DataType::QAsymmU8, armnn::DataType::Signed32 >()</h2>
1117
1118<div class="memitem">
1119<div class="memproto">
1120 <table class="memname">
1121 <tr>
1122 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef">Convolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
1123 <td>(</td>
1124 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1125 <td class="paramname">, </td>
1126 </tr>
1127 <tr>
1128 <td class="paramkey"></td>
1129 <td></td>
1130 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1131 <td class="paramname">, </td>
1132 </tr>
1133 <tr>
1134 <td class="paramkey"></td>
1135 <td></td>
1136 <td class="paramtype">bool&#160;</td>
1137 <td class="paramname">, </td>
1138 </tr>
1139 <tr>
1140 <td class="paramkey"></td>
1141 <td></td>
1142 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1143 <td class="paramname">&#160;</td>
1144 </tr>
1145 <tr>
1146 <td></td>
1147 <td>)</td>
1148 <td></td><td></td>
1149 </tr>
1150 </table>
1151</div><div class="memdoc">
1152
1153</div>
1154</div>
1155<a id="a48050c4e985c5741b51b55eb9961a19a"></a>
1156<h2 class="memtitle"><span class="permalink"><a href="#a48050c4e985c5741b51b55eb9961a19a">&#9670;&nbsp;</a></span>Convolution2d2x3x3Dilation3x3Test< armnn::DataType::QSymmS16, armnn::DataType::Signed32 >()</h2>
1157
1158<div class="memitem">
1159<div class="memproto">
1160 <table class="memname">
1161 <tr>
1162 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef">Convolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
1163 <td>(</td>
1164 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1165 <td class="paramname">, </td>
1166 </tr>
1167 <tr>
1168 <td class="paramkey"></td>
1169 <td></td>
1170 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1171 <td class="paramname">, </td>
1172 </tr>
1173 <tr>
1174 <td class="paramkey"></td>
1175 <td></td>
1176 <td class="paramtype">bool&#160;</td>
1177 <td class="paramname">, </td>
1178 </tr>
1179 <tr>
1180 <td class="paramkey"></td>
1181 <td></td>
1182 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1183 <td class="paramname">&#160;</td>
1184 </tr>
1185 <tr>
1186 <td></td>
1187 <td>)</td>
1188 <td></td><td></td>
1189 </tr>
1190 </table>
1191</div><div class="memdoc">
1192
1193</div>
1194</div>
1195<a id="a90abce368d7f16012bef5ee461329484"></a>
1196<h2 class="memtitle"><span class="permalink"><a href="#a90abce368d7f16012bef5ee461329484">&#9670;&nbsp;</a></span>Convolution2d3x3Dilation3x3Test()</h2>
1197
1198<div class="memitem">
1199<div class="memproto">
1200 <table class="memname">
1201 <tr>
1202 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Convolution2d3x3Dilation3x3Test </td>
1203 <td>(</td>
1204 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1205 <td class="paramname"><em>workloadFactory</em>, </td>
1206 </tr>
1207 <tr>
1208 <td class="paramkey"></td>
1209 <td></td>
1210 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1211 <td class="paramname"><em>memoryManager</em>, </td>
1212 </tr>
1213 <tr>
1214 <td class="paramkey"></td>
1215 <td></td>
1216 <td class="paramtype">bool&#160;</td>
1217 <td class="paramname"><em>biasEnabled</em>, </td>
1218 </tr>
1219 <tr>
1220 <td class="paramkey"></td>
1221 <td></td>
1222 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1223 <td class="paramname"><em>layout</em>&#160;</td>
1224 </tr>
1225 <tr>
1226 <td></td>
1227 <td>)</td>
1228 <td></td><td></td>
1229 </tr>
1230 </table>
1231</div><div class="memdoc">
1232
1233<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01083">1083</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1234<div class="fragment"><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>&#160;{</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 1, 10, 10}, ArmnnType);</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>&#160; std::vector&lt;float&gt; inputNoQuantizedValues =</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>&#160; {</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>&#160; };</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>&#160;</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>&#160; std::vector&lt;float&gt; kernelNoQuantizedValues =</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>&#160; {</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>&#160; 1, 2, 3,</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>&#160; 4, 5, 6,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>&#160; 7, 8, 9</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;</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>&#160; <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>&#160; <span class="comment">// therefore the output will be 4x4: (I−K+2P)/S +1 =&gt; (10-7 +0)/1 +1</span></div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>&#160; std::vector&lt;float&gt; outputExpectedNoQuantizedValues =</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; 6., 5., 5., 5.,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>&#160; 3., 2., 2., 2.</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>&#160; };</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="keywordflow">return</span> Convolution2d3x3DilationTestCommon&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>&#160; workloadFactory,</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>&#160; memoryManager,</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>&#160; inputNoQuantizedValues,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>&#160; inputTensorInfo,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>&#160; kernelNoQuantizedValues,</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>&#160; kernelTensorInfo,</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>&#160; outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>&#160; outputTensorInfo,</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>&#160; 3,</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>&#160; 3,</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>&#160; layout,</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>&#160; biasEnabled);</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</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>
1235</div><!-- fragment -->
1236</div>
1237</div>
1238<a id="a964c2340d3764cc09df574364ff2633c"></a>
1239<h2 class="memtitle"><span class="permalink"><a href="#a964c2340d3764cc09df574364ff2633c">&#9670;&nbsp;</a></span>Convolution2d3x3Dilation3x3Test< armnn::DataType::BFloat16, armnn::DataType::BFloat16 >()</h2>
1240
1241<div class="memitem">
1242<div class="memproto">
1243 <table class="memname">
1244 <tr>
1245 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a90abce368d7f16012bef5ee461329484">Convolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt; </td>
1246 <td>(</td>
1247 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1248 <td class="paramname">, </td>
1249 </tr>
1250 <tr>
1251 <td class="paramkey"></td>
1252 <td></td>
1253 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1254 <td class="paramname">, </td>
1255 </tr>
1256 <tr>
1257 <td class="paramkey"></td>
1258 <td></td>
1259 <td class="paramtype">bool&#160;</td>
1260 <td class="paramname">, </td>
1261 </tr>
1262 <tr>
1263 <td class="paramkey"></td>
1264 <td></td>
1265 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1266 <td class="paramname">&#160;</td>
1267 </tr>
1268 <tr>
1269 <td></td>
1270 <td>)</td>
1271 <td></td><td></td>
1272 </tr>
1273 </table>
1274</div><div class="memdoc">
1275
1276</div>
1277</div>
1278<a id="a7ea8f82c89483fdec102125b82a798c7"></a>
1279<h2 class="memtitle"><span class="permalink"><a href="#a7ea8f82c89483fdec102125b82a798c7">&#9670;&nbsp;</a></span>Convolution2d3x3Dilation3x3Test< armnn::DataType::Float32, armnn::DataType::Float32 >()</h2>
1280
1281<div class="memitem">
1282<div class="memproto">
1283 <table class="memname">
1284 <tr>
1285 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a90abce368d7f16012bef5ee461329484">Convolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt; </td>
1286 <td>(</td>
1287 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1288 <td class="paramname">, </td>
1289 </tr>
1290 <tr>
1291 <td class="paramkey"></td>
1292 <td></td>
1293 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1294 <td class="paramname">, </td>
1295 </tr>
1296 <tr>
1297 <td class="paramkey"></td>
1298 <td></td>
1299 <td class="paramtype">bool&#160;</td>
1300 <td class="paramname">, </td>
1301 </tr>
1302 <tr>
1303 <td class="paramkey"></td>
1304 <td></td>
1305 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1306 <td class="paramname">&#160;</td>
1307 </tr>
1308 <tr>
1309 <td></td>
1310 <td>)</td>
1311 <td></td><td></td>
1312 </tr>
1313 </table>
1314</div><div class="memdoc">
1315
1316</div>
1317</div>
1318<a id="ac580208ebb11ac2d93076a5a7a346b9f"></a>
1319<h2 class="memtitle"><span class="permalink"><a href="#ac580208ebb11ac2d93076a5a7a346b9f">&#9670;&nbsp;</a></span>Convolution2d3x3Dilation3x3Test< armnn::DataType::QAsymmU8, armnn::DataType::Signed32 >()</h2>
1320
1321<div class="memitem">
1322<div class="memproto">
1323 <table class="memname">
1324 <tr>
1325 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a90abce368d7f16012bef5ee461329484">Convolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
1326 <td>(</td>
1327 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1328 <td class="paramname">, </td>
1329 </tr>
1330 <tr>
1331 <td class="paramkey"></td>
1332 <td></td>
1333 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1334 <td class="paramname">, </td>
1335 </tr>
1336 <tr>
1337 <td class="paramkey"></td>
1338 <td></td>
1339 <td class="paramtype">bool&#160;</td>
1340 <td class="paramname">, </td>
1341 </tr>
1342 <tr>
1343 <td class="paramkey"></td>
1344 <td></td>
1345 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1346 <td class="paramname">&#160;</td>
1347 </tr>
1348 <tr>
1349 <td></td>
1350 <td>)</td>
1351 <td></td><td></td>
1352 </tr>
1353 </table>
1354</div><div class="memdoc">
1355
1356</div>
1357</div>
1358<a id="af84d6d89c899073318abbfa25292c36e"></a>
1359<h2 class="memtitle"><span class="permalink"><a href="#af84d6d89c899073318abbfa25292c36e">&#9670;&nbsp;</a></span>Convolution2d3x3Dilation3x3Test< armnn::DataType::QSymmS16, armnn::DataType::Signed32 >()</h2>
1360
1361<div class="memitem">
1362<div class="memproto">
1363 <table class="memname">
1364 <tr>
1365 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a90abce368d7f16012bef5ee461329484">Convolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
1366 <td>(</td>
1367 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1368 <td class="paramname">, </td>
1369 </tr>
1370 <tr>
1371 <td class="paramkey"></td>
1372 <td></td>
1373 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1374 <td class="paramname">, </td>
1375 </tr>
1376 <tr>
1377 <td class="paramkey"></td>
1378 <td></td>
1379 <td class="paramtype">bool&#160;</td>
1380 <td class="paramname">, </td>
1381 </tr>
1382 <tr>
1383 <td class="paramkey"></td>
1384 <td></td>
1385 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1386 <td class="paramname">&#160;</td>
1387 </tr>
1388 <tr>
1389 <td></td>
1390 <td>)</td>
1391 <td></td><td></td>
1392 </tr>
1393 </table>
1394</div><div class="memdoc">
1395
1396</div>
1397</div>
1398<a id="ad12c52b6d41931219bdfec5fbf5990bd"></a>
1399<h2 class="memtitle"><span class="permalink"><a href="#ad12c52b6d41931219bdfec5fbf5990bd">&#9670;&nbsp;</a></span>Convolution2d3x3DilationTestCommon()</h2>
1400
1401<div class="memitem">
1402<div class="memproto">
1403 <table class="memname">
1404 <tr>
1405 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Convolution2d3x3DilationTestCommon </td>
1406 <td>(</td>
1407 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1408 <td class="paramname"><em>workloadFactory</em>, </td>
1409 </tr>
1410 <tr>
1411 <td class="paramkey"></td>
1412 <td></td>
1413 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1414 <td class="paramname"><em>memoryManager</em>, </td>
1415 </tr>
1416 <tr>
1417 <td class="paramkey"></td>
1418 <td></td>
1419 <td class="paramtype">const std::vector&lt; float &gt; &amp;&#160;</td>
1420 <td class="paramname"><em>inputNoQuantizedValues</em>, </td>
1421 </tr>
1422 <tr>
1423 <td class="paramkey"></td>
1424 <td></td>
1425 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;&#160;</td>
1426 <td class="paramname"><em>inputTensorInfo</em>, </td>
1427 </tr>
1428 <tr>
1429 <td class="paramkey"></td>
1430 <td></td>
1431 <td class="paramtype">const std::vector&lt; float &gt; &amp;&#160;</td>
1432 <td class="paramname"><em>kernelNoQuantizedValues</em>, </td>
1433 </tr>
1434 <tr>
1435 <td class="paramkey"></td>
1436 <td></td>
1437 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;&#160;</td>
1438 <td class="paramname"><em>kernelTensorInfo</em>, </td>
1439 </tr>
1440 <tr>
1441 <td class="paramkey"></td>
1442 <td></td>
1443 <td class="paramtype">const std::vector&lt; float &gt; &amp;&#160;</td>
1444 <td class="paramname"><em>outputExpectedNoQuantizedValues</em>, </td>
1445 </tr>
1446 <tr>
1447 <td class="paramkey"></td>
1448 <td></td>
1449 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;&#160;</td>
1450 <td class="paramname"><em>outputTensorInfo</em>, </td>
1451 </tr>
1452 <tr>
1453 <td class="paramkey"></td>
1454 <td></td>
1455 <td class="paramtype">uint32_t&#160;</td>
1456 <td class="paramname"><em>dilationX</em>, </td>
1457 </tr>
1458 <tr>
1459 <td class="paramkey"></td>
1460 <td></td>
1461 <td class="paramtype">uint32_t&#160;</td>
1462 <td class="paramname"><em>dilationY</em>, </td>
1463 </tr>
1464 <tr>
1465 <td class="paramkey"></td>
1466 <td></td>
1467 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1468 <td class="paramname"><em>layout</em> = <code><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></code>, </td>
1469 </tr>
1470 <tr>
1471 <td class="paramkey"></td>
1472 <td></td>
1473 <td class="paramtype">uint32_t&#160;</td>
1474 <td class="paramname"><em>padLeft</em> = <code>0</code>, </td>
1475 </tr>
1476 <tr>
1477 <td class="paramkey"></td>
1478 <td></td>
1479 <td class="paramtype">uint32_t&#160;</td>
1480 <td class="paramname"><em>padTop</em> = <code>0</code>, </td>
1481 </tr>
1482 <tr>
1483 <td class="paramkey"></td>
1484 <td></td>
1485 <td class="paramtype">uint32_t&#160;</td>
1486 <td class="paramname"><em>padRight</em> = <code>0</code>, </td>
1487 </tr>
1488 <tr>
1489 <td class="paramkey"></td>
1490 <td></td>
1491 <td class="paramtype">uint32_t&#160;</td>
1492 <td class="paramname"><em>padBottom</em> = <code>0</code>, </td>
1493 </tr>
1494 <tr>
1495 <td class="paramkey"></td>
1496 <td></td>
1497 <td class="paramtype">uint32_t&#160;</td>
1498 <td class="paramname"><em>strideX</em> = <code>1</code>, </td>
1499 </tr>
1500 <tr>
1501 <td class="paramkey"></td>
1502 <td></td>
1503 <td class="paramtype">uint32_t&#160;</td>
1504 <td class="paramname"><em>strideY</em> = <code>1</code>, </td>
1505 </tr>
1506 <tr>
1507 <td class="paramkey"></td>
1508 <td></td>
1509 <td class="paramtype">bool&#160;</td>
1510 <td class="paramname"><em>biasEnabled</em> = <code><a class="el" href="_ref_layer_tests_8cpp.xhtml#af3b727ae5a13ff472892ab8bda2eb1b5">false</a></code>&#160;</td>
1511 </tr>
1512 <tr>
1513 <td></td>
1514 <td>)</td>
1515 <td></td><td></td>
1516 </tr>
1517 </table>
1518</div><div class="memdoc">
1519
1520<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00995">995</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1521
1522<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::QSymmS16</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00275">TensorInfo::SetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
1523<div class="fragment"><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160;{</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160; <span class="keywordtype">float</span> qScale;</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160; int32_t qOffset;</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>&#160; <span class="keywordflow">switch</span> (ArmnnType)</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; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</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; qScale = 0.1f;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>&#160; qOffset = 128;</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>&#160; }</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</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; qScale = 0.1f;</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>&#160; qOffset = 0;</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>&#160; }</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>&#160; {</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>&#160; qScale = 0.f;</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>&#160; qOffset = 0;</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>&#160; <span class="keywordflow">break</span>;</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; }</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>&#160;</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>&#160; kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>&#160; kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</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="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo,</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>&#160; std::vector&lt;T&gt;(QuantizedVector&lt;T&gt;(inputNoQuantizedValues,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>&#160; inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>&#160; inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>&#160; <span class="keyword">auto</span> kernel = MakeTensor&lt;T, 4&gt;(kernelTensorInfo,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>&#160; std::vector&lt;T&gt;(QuantizedVector&lt;T&gt;(kernelNoQuantizedValues,</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>&#160; kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>&#160; kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>&#160; <span class="keyword">auto</span> expectedOutput =</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>&#160; MakeTensor&lt;T, 4&gt;(outputTensorInfo,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>&#160; std::vector&lt;T&gt;(QuantizedVector&lt;T&gt;(outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>&#160; outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>&#160; outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>&#160;</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>&#160; workloadFactory,</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>&#160; memoryManager,</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>&#160; input,</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>&#160; kernel,</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>&#160; GetBias2&lt;ArmnnBType&gt;(biasEnabled, qScale * qScale),</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>&#160; expectedOutput,</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>&#160; qScale,</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>&#160; qOffset,</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>&#160; layout,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>&#160; padLeft,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>&#160; padTop,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>&#160; padRight,</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>&#160; padBottom,</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>&#160; strideX,</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>&#160; strideY,</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>&#160; dilationX,</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>&#160; dilationY);</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>&#160;}</div><div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
1524<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
1525<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>
1526<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
1527<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div>
1528</div><!-- fragment -->
1529</div>
1530</div>
1531<a id="a48884a37a6b783185c608a68cfce752f"></a>
1532<h2 class="memtitle"><span class="permalink"><a href="#a48884a37a6b783185c608a68cfce752f">&#9670;&nbsp;</a></span>Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest()</h2>
1533
1534<div class="memitem">
1535<div class="memproto">
1536 <table class="memname">
1537 <tr>
1538 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest </td>
1539 <td>(</td>
1540 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1541 <td class="paramname"><em>workloadFactory</em>, </td>
1542 </tr>
1543 <tr>
1544 <td class="paramkey"></td>
1545 <td></td>
1546 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1547 <td class="paramname"><em>memoryManager</em>, </td>
1548 </tr>
1549 <tr>
1550 <td class="paramkey"></td>
1551 <td></td>
1552 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1553 <td class="paramname"><em>layout</em>&#160;</td>
1554 </tr>
1555 <tr>
1556 <td></td>
1557 <td>)</td>
1558 <td></td><td></td>
1559 </tr>
1560 </table>
1561</div><div class="memdoc">
1562
1563<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03064">3064</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1564
1565<p class="reference">References <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00869">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon()</a>, and <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>.</p>
1566<div class="fragment"><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>&#160;{</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>&#160; <span class="keywordflow">return</span> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a35ad1225c524b4594b461e613695ee4a">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon</a></div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>&#160; &lt;<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>&#160; workloadFactory, memoryManager, layout, 0.0f, 0);</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>&#160;}</div><div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a35ad1225c524b4594b461e613695ee4a"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a35ad1225c524b4594b461e613695ee4a">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00869">Conv2dTestImpl.cpp:869</a></div></div>
1567<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
1568</div><!-- fragment -->
1569</div>
1570</div>
1571<a id="a35ad1225c524b4594b461e613695ee4a"></a>
1572<h2 class="memtitle"><span class="permalink"><a href="#a35ad1225c524b4594b461e613695ee4a">&#9670;&nbsp;</a></span>Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon()</h2>
1573
1574<div class="memitem">
1575<div class="memproto">
1576 <table class="memname">
1577 <tr>
1578 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon </td>
1579 <td>(</td>
1580 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1581 <td class="paramname"><em>workloadFactory</em>, </td>
1582 </tr>
1583 <tr>
1584 <td class="paramkey"></td>
1585 <td></td>
1586 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1587 <td class="paramname"><em>memoryManager</em>, </td>
1588 </tr>
1589 <tr>
1590 <td class="paramkey"></td>
1591 <td></td>
1592 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1593 <td class="paramname"><em>layout</em>, </td>
1594 </tr>
1595 <tr>
1596 <td class="paramkey"></td>
1597 <td></td>
1598 <td class="paramtype">float&#160;</td>
1599 <td class="paramname"><em>qScale</em>, </td>
1600 </tr>
1601 <tr>
1602 <td class="paramkey"></td>
1603 <td></td>
1604 <td class="paramtype">int32_t&#160;</td>
1605 <td class="paramname"><em>qOffset</em>&#160;</td>
1606 </tr>
1607 <tr>
1608 <td></td>
1609 <td>)</td>
1610 <td></td><td></td>
1611 </tr>
1612 </table>
1613</div><div class="memdoc">
1614
1615<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00869">869</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1616
1617<p class="reference">Referenced by <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03064">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest()</a>.</p>
1618<div class="fragment"><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; <span class="comment">// Use a single-batch 1-channel 3x3 image as input.</span></div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>&#160; boost::multi_array&lt;T, 4&gt; input = MakeTensor&lt;T, 4&gt;(inputDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>&#160; 11,21,31,</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>&#160; 12,22,32,</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>&#160; 13,23,33</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>&#160; },</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>&#160; qScale, qOffset)));</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>&#160;</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>&#160; <span class="comment">// Use 1 batch of a 1-channel 2x2 kernel.</span></div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({1, 1, 2, 2}, ArmnnType);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>&#160; boost::multi_array&lt;T, 4&gt; kernel = MakeTensor&lt;T, 4&gt;(kernelDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>&#160; -11,-21,</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>&#160; -12,-22,</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; qScale, qOffset)));</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">// Expected output is 1 batch of a 1-channel 6x8 image.</span></div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>&#160;<span class="comment">// Manually calculated like this:</span></div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>&#160;<span class="comment">//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]</span></div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>&#160;<span class="comment">//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..]</span></div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>&#160;<span class="comment">//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..]</span></div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>&#160;<span class="comment">//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..]</span></div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>&#160;<span class="comment">//[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..]</span></div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>&#160;<span class="comment">//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]</span></div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>&#160;<span class="comment">//[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..]</span></div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 1, 8, 6}, ArmnnType);</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>&#160; 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>&#160; -242, -594, -934, -372, 0, 0,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>&#160; -495, -1190, -1850, -725, 0, 0,</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>&#160; -538, -1256, -1916, -748, 0, 0,</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>&#160; -273, -626, -946, -363, 0, 0,</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>&#160; 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>&#160; 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>&#160; 0, 0, 0, 0, 0, 0</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; qScale, qOffset)));</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>&#160;</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>&#160; workloadFactory,</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>&#160; memoryManager,</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>&#160; input,</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>&#160; kernel,</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>&#160; GetBias2&lt;ArmnnBType&gt;(<span class="keyword">false</span>, qScale * qScale),</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>&#160; expectedOutput,</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>&#160; qScale,</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>&#160; qOffset,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>&#160; layout,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>&#160; 1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>&#160; 2, <span class="comment">// Padding top.</span></div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>&#160; 3, <span class="comment">// Padding right.</span></div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>&#160; 4); <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l00932"></a><span class="lineno"> 932</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>
1619</div><!-- fragment -->
1620</div>
1621</div>
1622<a id="af7f2cd23423130ebdd916de12bc0eb1d"></a>
1623<h2 class="memtitle"><span class="permalink"><a href="#af7f2cd23423130ebdd916de12bc0eb1d">&#9670;&nbsp;</a></span>Convolution2dAsymmetricPaddingTest()</h2>
1624
1625<div class="memitem">
1626<div class="memproto">
1627 <table class="memname">
1628 <tr>
1629 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; Convolution2dAsymmetricPaddingTest </td>
1630 <td>(</td>
1631 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1632 <td class="paramname"><em>workloadFactory</em>, </td>
1633 </tr>
1634 <tr>
1635 <td class="paramkey"></td>
1636 <td></td>
1637 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1638 <td class="paramname"><em>memoryManager</em>, </td>
1639 </tr>
1640 <tr>
1641 <td class="paramkey"></td>
1642 <td></td>
1643 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1644 <td class="paramname"><em>layout</em>&#160;</td>
1645 </tr>
1646 <tr>
1647 <td></td>
1648 <td>)</td>
1649 <td></td><td></td>
1650 </tr>
1651 </table>
1652</div><div class="memdoc">
1653
1654<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03055">3055</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1655<div class="fragment"><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span>&#160;{</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2dAsymmetricPaddingTestCommon&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>&#160; workloadFactory, memoryManager, layout, 0.0f, 0);</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>&#160;}</div></div><!-- fragment -->
1656</div>
1657</div>
1658<a id="a370a5216668b507284677234264a22a2"></a>
1659<h2 class="memtitle"><span class="permalink"><a href="#a370a5216668b507284677234264a22a2">&#9670;&nbsp;</a></span>Convolution2dPerAxisQuantTest()</h2>
1660
1661<div class="memitem">
1662<div class="memproto">
1663 <table class="memname">
1664 <tr>
1665 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; Convolution2dPerAxisQuantTest </td>
1666 <td>(</td>
1667 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1668 <td class="paramname"><em>workloadFactory</em>, </td>
1669 </tr>
1670 <tr>
1671 <td class="paramkey"></td>
1672 <td></td>
1673 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1674 <td class="paramname"><em>memoryManager</em>, </td>
1675 </tr>
1676 <tr>
1677 <td class="paramkey"></td>
1678 <td></td>
1679 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1680 <td class="paramname"><em>layout</em>&#160;</td>
1681 </tr>
1682 <tr>
1683 <td></td>
1684 <td>)</td>
1685 <td></td><td></td>
1686 </tr>
1687 </table>
1688</div><div class="memdoc">
1689
1690<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03092">3092</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1691
1692<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <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#l01159">IWorkloadFactory::CreateConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00438">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00440">Convolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00428">Convolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00422">Convolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00424">Convolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00426">Convolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00430">Convolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00432">Convolution2dDescriptor::m_StrideY</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</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="_data_layout_utils_8hpp_source.xhtml#l00026">PermuteTensorNhwcToNchw()</a>.</p>
1693<div class="fragment"><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span>&#160;{</div><div class="line"><a name="l03097"></a><span class="lineno"> 3097</span>&#160; <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l03098"></a><span class="lineno"> 3098</span>&#160;</div><div class="line"><a name="l03099"></a><span class="lineno"> 3099</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>;</div><div class="line"><a name="l03100"></a><span class="lineno"> 3100</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> kernelType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>;</div><div class="line"><a name="l03101"></a><span class="lineno"> 3101</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> biasType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>;</div><div class="line"><a name="l03102"></a><span class="lineno"> 3102</span>&#160;</div><div class="line"><a name="l03103"></a><span class="lineno"> 3103</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo ({ 1, 3, 1, 2 }, inputType, 0.5f, 128);</div><div class="line"><a name="l03104"></a><span class="lineno"> 3104</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ 1, 3, 1, 3 }, inputType, 1.0f, 128);</div><div class="line"><a name="l03105"></a><span class="lineno"> 3105</span>&#160;</div><div class="line"><a name="l03106"></a><span class="lineno"> 3106</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; quantScales{ 0.5f, 0.75f, 1.0f };</div><div class="line"><a name="l03107"></a><span class="lineno"> 3107</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> quantDimension = 0;</div><div class="line"><a name="l03108"></a><span class="lineno"> 3108</span>&#160;</div><div class="line"><a name="l03109"></a><span class="lineno"> 3109</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> kernelInfo({ 3, 1, 1, 2 }, kernelType, quantScales, quantDimension);</div><div class="line"><a name="l03110"></a><span class="lineno"> 3110</span>&#160;</div><div class="line"><a name="l03111"></a><span class="lineno"> 3111</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; biasQuantScales{ 0.25f, 0.375f, 0.5f };</div><div class="line"><a name="l03112"></a><span class="lineno"> 3112</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({ 3 }, biasType, biasQuantScales, quantDimension);</div><div class="line"><a name="l03113"></a><span class="lineno"> 3113</span>&#160;</div><div class="line"><a name="l03114"></a><span class="lineno"> 3114</span>&#160; std::vector&lt;uint8_t&gt; inputData =</div><div class="line"><a name="l03115"></a><span class="lineno"> 3115</span>&#160; {</div><div class="line"><a name="l03116"></a><span class="lineno"> 3116</span>&#160; 138, 108, 138, 108, 138, 108</div><div class="line"><a name="l03117"></a><span class="lineno"> 3117</span>&#160; };</div><div class="line"><a name="l03118"></a><span class="lineno"> 3118</span>&#160;</div><div class="line"><a name="l03119"></a><span class="lineno"> 3119</span>&#160; std::vector&lt;int8_t&gt; kernelData =</div><div class="line"><a name="l03120"></a><span class="lineno"> 3120</span>&#160; {</div><div class="line"><a name="l03121"></a><span class="lineno"> 3121</span>&#160; 1, 2, 1, 2, 1, 2</div><div class="line"><a name="l03122"></a><span class="lineno"> 3122</span>&#160; };</div><div class="line"><a name="l03123"></a><span class="lineno"> 3123</span>&#160;</div><div class="line"><a name="l03124"></a><span class="lineno"> 3124</span>&#160; std::vector&lt;int32_t&gt; biasData =</div><div class="line"><a name="l03125"></a><span class="lineno"> 3125</span>&#160; {</div><div class="line"><a name="l03126"></a><span class="lineno"> 3126</span>&#160; 4, 4, 4</div><div class="line"><a name="l03127"></a><span class="lineno"> 3127</span>&#160; };</div><div class="line"><a name="l03128"></a><span class="lineno"> 3128</span>&#160;</div><div class="line"><a name="l03129"></a><span class="lineno"> 3129</span>&#160; std::vector&lt;uint8_t&gt; expectedOutputData =</div><div class="line"><a name="l03130"></a><span class="lineno"> 3130</span>&#160; {</div><div class="line"><a name="l03131"></a><span class="lineno"> 3131</span>&#160; 121, 118, 115, 121, 118, 115, 121, 118, 115</div><div class="line"><a name="l03132"></a><span class="lineno"> 3132</span>&#160; };</div><div class="line"><a name="l03133"></a><span class="lineno"> 3133</span>&#160;</div><div class="line"><a name="l03134"></a><span class="lineno"> 3134</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l03135"></a><span class="lineno"> 3135</span>&#160; {</div><div class="line"><a name="l03136"></a><span class="lineno"> 3136</span>&#160; <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(inputInfo, inputData);</div><div class="line"><a name="l03137"></a><span class="lineno"> 3137</span>&#160; <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(kernelInfo, kernelData);</div><div class="line"><a name="l03138"></a><span class="lineno"> 3138</span>&#160; <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(outputInfo, expectedOutputData);</div><div class="line"><a name="l03139"></a><span class="lineno"> 3139</span>&#160; }</div><div class="line"><a name="l03140"></a><span class="lineno"> 3140</span>&#160;</div><div class="line"><a name="l03141"></a><span class="lineno"> 3141</span>&#160; <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l03142"></a><span class="lineno"> 3142</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l03143"></a><span class="lineno"> 3143</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l03144"></a><span class="lineno"> 3144</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l03145"></a><span class="lineno"> 3145</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l03146"></a><span class="lineno"> 3146</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l03147"></a><span class="lineno"> 3147</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l03148"></a><span class="lineno"> 3148</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03149"></a><span class="lineno"> 3149</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l03150"></a><span class="lineno"> 3150</span>&#160;</div><div class="line"><a name="l03151"></a><span class="lineno"> 3151</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputInfo);</div><div class="line"><a name="l03152"></a><span class="lineno"> 3152</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputInfo);</div><div class="line"><a name="l03153"></a><span class="lineno"> 3153</span>&#160;</div><div class="line"><a name="l03154"></a><span class="lineno"> 3154</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l03155"></a><span class="lineno"> 3155</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(kernelInfo);</div><div class="line"><a name="l03156"></a><span class="lineno"> 3156</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasTensor(biasInfo);</div><div class="line"><a name="l03157"></a><span class="lineno"> 3157</span>&#160;</div><div class="line"><a name="l03158"></a><span class="lineno"> 3158</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightTensor, kernelData.data());</div><div class="line"><a name="l03159"></a><span class="lineno"> 3159</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, biasData.data());</div><div class="line"><a name="l03160"></a><span class="lineno"> 3160</span>&#160;</div><div class="line"><a name="l03161"></a><span class="lineno"> 3161</span>&#160; <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">Convolution2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l03162"></a><span class="lineno"> 3162</span>&#160; queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = descriptor;</div><div class="line"><a name="l03163"></a><span class="lineno"> 3163</span>&#160; queueDescriptor.m_Weight = &amp;weightTensor;</div><div class="line"><a name="l03164"></a><span class="lineno"> 3164</span>&#160; queueDescriptor.m_Bias = &amp;biasTensor;</div><div class="line"><a name="l03165"></a><span class="lineno"> 3165</span>&#160;</div><div class="line"><a name="l03166"></a><span class="lineno"> 3166</span>&#160; AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());</div><div class="line"><a name="l03167"></a><span class="lineno"> 3167</span>&#160; AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());</div><div class="line"><a name="l03168"></a><span class="lineno"> 3168</span>&#160;</div><div class="line"><a name="l03169"></a><span class="lineno"> 3169</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(queueDescriptor, workloadInfo);</div><div class="line"><a name="l03170"></a><span class="lineno"> 3170</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l03171"></a><span class="lineno"> 3171</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l03172"></a><span class="lineno"> 3172</span>&#160;</div><div class="line"><a name="l03173"></a><span class="lineno"> 3173</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData.data());</div><div class="line"><a name="l03174"></a><span class="lineno"> 3174</span>&#160;</div><div class="line"><a name="l03175"></a><span class="lineno"> 3175</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l03176"></a><span class="lineno"> 3176</span>&#160;</div><div class="line"><a name="l03177"></a><span class="lineno"> 3177</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> ret(outputInfo);</div><div class="line"><a name="l03178"></a><span class="lineno"> 3178</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(ret.output.origin(), outputHandle.get());</div><div class="line"><a name="l03179"></a><span class="lineno"> 3179</span>&#160; ret.outputExpected = MakeTensor&lt;uint8_t, 4&gt;(outputInfo, expectedOutputData);</div><div class="line"><a name="l03180"></a><span class="lineno"> 3180</span>&#160;</div><div class="line"><a name="l03181"></a><span class="lineno"> 3181</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l03182"></a><span class="lineno"> 3182</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div>
1694<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div>
1695<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div>
1696<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>
1697<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</a></div></div>
1698<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
1699<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div>
1700<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div>
1701<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>
1702<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div>
1703<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div>
1704<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
1705<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
1706<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
1707<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>
1708<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div>
1709<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>
1710<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div>
1711<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
1712<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>
1713<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>
1714<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
1715<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
1716<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div>
1717<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2184995027cd2c9f9980206de9658855"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">armnn::IWorkloadFactory::CreateConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConvolution2d(const Convolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01159">WorkloadFactory.cpp:1159</a></div></div>
1718<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>
1719<div class="ttc" id="_data_layout_utils_8hpp_xhtml_a1452f049aef30409b3b649af2be7ff82"><div class="ttname"><a href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a></div><div class="ttdeci">void PermuteTensorNhwcToNchw(armnn::TensorInfo &amp;tensorInfo, std::vector&lt; T &gt; &amp;tensorData)</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_utils_8hpp_source.xhtml#l00026">DataLayoutUtils.hpp:26</a></div></div>
1720</div><!-- fragment -->
1721</div>
1722</div>
1723<a id="acffa50ae3185e3e5302909f27e7e9a02"></a>
1724<h2 class="memtitle"><span class="permalink"><a href="#acffa50ae3185e3e5302909f27e7e9a02">&#9670;&nbsp;</a></span>DepthwiseConvolution2d2x3x3Dilation3x3Test()</h2>
1725
1726<div class="memitem">
1727<div class="memproto">
1728 <table class="memname">
1729 <tr>
1730 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2d2x3x3Dilation3x3Test </td>
1731 <td>(</td>
1732 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1733 <td class="paramname"><em>workloadFactory</em>, </td>
1734 </tr>
1735 <tr>
1736 <td class="paramkey"></td>
1737 <td></td>
1738 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1739 <td class="paramname"><em>memoryManager</em>, </td>
1740 </tr>
1741 <tr>
1742 <td class="paramkey"></td>
1743 <td></td>
1744 <td class="paramtype">bool&#160;</td>
1745 <td class="paramname"><em>biasEnabled</em>, </td>
1746 </tr>
1747 <tr>
1748 <td class="paramkey"></td>
1749 <td></td>
1750 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1751 <td class="paramname"><em>layout</em>&#160;</td>
1752 </tr>
1753 <tr>
1754 <td></td>
1755 <td>)</td>
1756 <td></td><td></td>
1757 </tr>
1758 </table>
1759</div><div class="memdoc">
1760
1761<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02432">2432</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1762<div class="fragment"><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>&#160;{</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 10, 10}, ArmnnType);</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>&#160; std::vector&lt;float&gt; inputNoQuantizedValues =</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>&#160; {</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>&#160;</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>&#160; };</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>&#160;</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>&#160; std::vector&lt;float&gt; kernelNoQuantizedValues =</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>&#160; {</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>&#160; 7, 8, 9,</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; 1, 2, 3,</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>&#160; 7, 8, 9</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>&#160; };</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>&#160;</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>&#160; <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>&#160; <span class="comment">// therefore the output will be 2x4x4: (I−K+2P)/S +1 =&gt; (10-7 +0)/1 +1</span></div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2, 4, 4}, ArmnnType);</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>&#160; std::vector&lt;float&gt; outputExpectedNoQuantizedValues =</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>&#160; {</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>&#160; 3., 2., 2., 2.,</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; 6., 5., 5., 5.,</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>&#160; 3., 2., 2., 2.</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span>&#160; };</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; <span class="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>&#160; workloadFactory,</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>&#160; memoryManager,</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>&#160; inputNoQuantizedValues,</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>&#160; inputTensorInfo,</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>&#160; kernelNoQuantizedValues,</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>&#160; kernelTensorInfo,</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>&#160; outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>&#160; outputTensorInfo,</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>&#160; 3,</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>&#160; 3,</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>&#160; layout,</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>&#160; biasEnabled);</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</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>
1763</div><!-- fragment -->
1764</div>
1765</div>
1766<a id="a0c016403b54cf7386462b18a01e49a60"></a>
1767<h2 class="memtitle"><span class="permalink"><a href="#a0c016403b54cf7386462b18a01e49a60">&#9670;&nbsp;</a></span>DepthwiseConvolution2d2x3x3Dilation3x3Test< armnn::DataType::BFloat16, armnn::DataType::BFloat16 >()</h2>
1768
1769<div class="memitem">
1770<div class="memproto">
1771 <table class="memname">
1772 <tr>
1773 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02">DepthwiseConvolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt; </td>
1774 <td>(</td>
1775 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1776 <td class="paramname">, </td>
1777 </tr>
1778 <tr>
1779 <td class="paramkey"></td>
1780 <td></td>
1781 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1782 <td class="paramname">, </td>
1783 </tr>
1784 <tr>
1785 <td class="paramkey"></td>
1786 <td></td>
1787 <td class="paramtype">bool&#160;</td>
1788 <td class="paramname">, </td>
1789 </tr>
1790 <tr>
1791 <td class="paramkey"></td>
1792 <td></td>
1793 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1794 <td class="paramname">&#160;</td>
1795 </tr>
1796 <tr>
1797 <td></td>
1798 <td>)</td>
1799 <td></td><td></td>
1800 </tr>
1801 </table>
1802</div><div class="memdoc">
1803
1804</div>
1805</div>
1806<a id="abfba475aaa254cb80fea6f6b9e2885ed"></a>
1807<h2 class="memtitle"><span class="permalink"><a href="#abfba475aaa254cb80fea6f6b9e2885ed">&#9670;&nbsp;</a></span>DepthwiseConvolution2d2x3x3Dilation3x3Test< armnn::DataType::Float32, armnn::DataType::Float32 >()</h2>
1808
1809<div class="memitem">
1810<div class="memproto">
1811 <table class="memname">
1812 <tr>
1813 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02">DepthwiseConvolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt; </td>
1814 <td>(</td>
1815 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1816 <td class="paramname">, </td>
1817 </tr>
1818 <tr>
1819 <td class="paramkey"></td>
1820 <td></td>
1821 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1822 <td class="paramname">, </td>
1823 </tr>
1824 <tr>
1825 <td class="paramkey"></td>
1826 <td></td>
1827 <td class="paramtype">bool&#160;</td>
1828 <td class="paramname">, </td>
1829 </tr>
1830 <tr>
1831 <td class="paramkey"></td>
1832 <td></td>
1833 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1834 <td class="paramname">&#160;</td>
1835 </tr>
1836 <tr>
1837 <td></td>
1838 <td>)</td>
1839 <td></td><td></td>
1840 </tr>
1841 </table>
1842</div><div class="memdoc">
1843
1844</div>
1845</div>
1846<a id="a7d1005e18161a898d383f302bda746ea"></a>
1847<h2 class="memtitle"><span class="permalink"><a href="#a7d1005e18161a898d383f302bda746ea">&#9670;&nbsp;</a></span>DepthwiseConvolution2d2x3x3Dilation3x3Test< armnn::DataType::QAsymmU8, armnn::DataType::Signed32 >()</h2>
1848
1849<div class="memitem">
1850<div class="memproto">
1851 <table class="memname">
1852 <tr>
1853 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02">DepthwiseConvolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
1854 <td>(</td>
1855 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1856 <td class="paramname">, </td>
1857 </tr>
1858 <tr>
1859 <td class="paramkey"></td>
1860 <td></td>
1861 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1862 <td class="paramname">, </td>
1863 </tr>
1864 <tr>
1865 <td class="paramkey"></td>
1866 <td></td>
1867 <td class="paramtype">bool&#160;</td>
1868 <td class="paramname">, </td>
1869 </tr>
1870 <tr>
1871 <td class="paramkey"></td>
1872 <td></td>
1873 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1874 <td class="paramname">&#160;</td>
1875 </tr>
1876 <tr>
1877 <td></td>
1878 <td>)</td>
1879 <td></td><td></td>
1880 </tr>
1881 </table>
1882</div><div class="memdoc">
1883
1884</div>
1885</div>
1886<a id="adc98546ccc8455972832038cf8a296c9"></a>
1887<h2 class="memtitle"><span class="permalink"><a href="#adc98546ccc8455972832038cf8a296c9">&#9670;&nbsp;</a></span>DepthwiseConvolution2d2x3x3Dilation3x3Test< armnn::DataType::QSymmS16, armnn::DataType::Signed32 >()</h2>
1888
1889<div class="memitem">
1890<div class="memproto">
1891 <table class="memname">
1892 <tr>
1893 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02">DepthwiseConvolution2d2x3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
1894 <td>(</td>
1895 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1896 <td class="paramname">, </td>
1897 </tr>
1898 <tr>
1899 <td class="paramkey"></td>
1900 <td></td>
1901 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1902 <td class="paramname">, </td>
1903 </tr>
1904 <tr>
1905 <td class="paramkey"></td>
1906 <td></td>
1907 <td class="paramtype">bool&#160;</td>
1908 <td class="paramname">, </td>
1909 </tr>
1910 <tr>
1911 <td class="paramkey"></td>
1912 <td></td>
1913 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1914 <td class="paramname">&#160;</td>
1915 </tr>
1916 <tr>
1917 <td></td>
1918 <td>)</td>
1919 <td></td><td></td>
1920 </tr>
1921 </table>
1922</div><div class="memdoc">
1923
1924</div>
1925</div>
1926<a id="a1c3398bdb48e4ce4643a1eeaf3e054a3"></a>
1927<h2 class="memtitle"><span class="permalink"><a href="#a1c3398bdb48e4ce4643a1eeaf3e054a3">&#9670;&nbsp;</a></span>DepthwiseConvolution2d3x3Dilation3x3Test()</h2>
1928
1929<div class="memitem">
1930<div class="memproto">
1931 <table class="memname">
1932 <tr>
1933 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2d3x3Dilation3x3Test </td>
1934 <td>(</td>
1935 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1936 <td class="paramname"><em>workloadFactory</em>, </td>
1937 </tr>
1938 <tr>
1939 <td class="paramkey"></td>
1940 <td></td>
1941 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1942 <td class="paramname"><em>memoryManager</em>, </td>
1943 </tr>
1944 <tr>
1945 <td class="paramkey"></td>
1946 <td></td>
1947 <td class="paramtype">bool&#160;</td>
1948 <td class="paramname"><em>biasEnabled</em>, </td>
1949 </tr>
1950 <tr>
1951 <td class="paramkey"></td>
1952 <td></td>
1953 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1954 <td class="paramname"><em>layout</em>&#160;</td>
1955 </tr>
1956 <tr>
1957 <td></td>
1958 <td>)</td>
1959 <td></td><td></td>
1960 </tr>
1961 </table>
1962</div><div class="memdoc">
1963
1964<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02376">2376</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
1965<div class="fragment"><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>&#160;{</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 1, 10, 10}, ArmnnType);</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>&#160; std::vector&lt;float&gt; inputNoQuantizedValues =</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>&#160; {</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>&#160; 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>&#160; std::vector&lt;float&gt; kernelNoQuantizedValues =</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>&#160; {</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>&#160; 7, 8, 9</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>&#160; };</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>&#160;</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>&#160; <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>&#160; <span class="comment">// therefore the output will be 4x4: (I−K+2P)/S +1 =&gt; (10-7 +0)/1 +1</span></div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>&#160; std::vector&lt;float&gt; outputExpectedNoQuantizedValues =</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; 6., 5., 5., 5.,</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>&#160; 6., 5., 5., 5.,</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>&#160; 3., 2., 2., 2.</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>&#160; };</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>&#160;</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>&#160; workloadFactory,</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>&#160; memoryManager,</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>&#160; inputNoQuantizedValues,</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>&#160; inputTensorInfo,</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>&#160; kernelNoQuantizedValues,</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>&#160; kernelTensorInfo,</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>&#160; outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>&#160; outputTensorInfo,</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>&#160; 3,</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>&#160; 3,</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>&#160; layout,</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>&#160; biasEnabled);</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</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>
1966</div><!-- fragment -->
1967</div>
1968</div>
1969<a id="a003cb9146f0c41e02eedcd250546ba74"></a>
1970<h2 class="memtitle"><span class="permalink"><a href="#a003cb9146f0c41e02eedcd250546ba74">&#9670;&nbsp;</a></span>DepthwiseConvolution2d3x3Dilation3x3Test< armnn::DataType::BFloat16, armnn::DataType::BFloat16 >()</h2>
1971
1972<div class="memitem">
1973<div class="memproto">
1974 <table class="memname">
1975 <tr>
1976 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3">DepthwiseConvolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt; </td>
1977 <td>(</td>
1978 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
1979 <td class="paramname">, </td>
1980 </tr>
1981 <tr>
1982 <td class="paramkey"></td>
1983 <td></td>
1984 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
1985 <td class="paramname">, </td>
1986 </tr>
1987 <tr>
1988 <td class="paramkey"></td>
1989 <td></td>
1990 <td class="paramtype">bool&#160;</td>
1991 <td class="paramname">, </td>
1992 </tr>
1993 <tr>
1994 <td class="paramkey"></td>
1995 <td></td>
1996 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
1997 <td class="paramname">&#160;</td>
1998 </tr>
1999 <tr>
2000 <td></td>
2001 <td>)</td>
2002 <td></td><td></td>
2003 </tr>
2004 </table>
2005</div><div class="memdoc">
2006
2007</div>
2008</div>
2009<a id="a5d3f9d15fbc0e3f43e100efb545e6ce6"></a>
2010<h2 class="memtitle"><span class="permalink"><a href="#a5d3f9d15fbc0e3f43e100efb545e6ce6">&#9670;&nbsp;</a></span>DepthwiseConvolution2d3x3Dilation3x3Test< armnn::DataType::Float32, armnn::DataType::Float32 >()</h2>
2011
2012<div class="memitem">
2013<div class="memproto">
2014 <table class="memname">
2015 <tr>
2016 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3">DepthwiseConvolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt; </td>
2017 <td>(</td>
2018 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2019 <td class="paramname">, </td>
2020 </tr>
2021 <tr>
2022 <td class="paramkey"></td>
2023 <td></td>
2024 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2025 <td class="paramname">, </td>
2026 </tr>
2027 <tr>
2028 <td class="paramkey"></td>
2029 <td></td>
2030 <td class="paramtype">bool&#160;</td>
2031 <td class="paramname">, </td>
2032 </tr>
2033 <tr>
2034 <td class="paramkey"></td>
2035 <td></td>
2036 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2037 <td class="paramname">&#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</div>
2048</div>
2049<a id="a7703f4745f048b3a0b0c082b01d9715e"></a>
2050<h2 class="memtitle"><span class="permalink"><a href="#a7703f4745f048b3a0b0c082b01d9715e">&#9670;&nbsp;</a></span>DepthwiseConvolution2d3x3Dilation3x3Test< armnn::DataType::QAsymmU8, armnn::DataType::Signed32 >()</h2>
2051
2052<div class="memitem">
2053<div class="memproto">
2054 <table class="memname">
2055 <tr>
2056 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3">DepthwiseConvolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
2057 <td>(</td>
2058 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2059 <td class="paramname">, </td>
2060 </tr>
2061 <tr>
2062 <td class="paramkey"></td>
2063 <td></td>
2064 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2065 <td class="paramname">, </td>
2066 </tr>
2067 <tr>
2068 <td class="paramkey"></td>
2069 <td></td>
2070 <td class="paramtype">bool&#160;</td>
2071 <td class="paramname">, </td>
2072 </tr>
2073 <tr>
2074 <td class="paramkey"></td>
2075 <td></td>
2076 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2077 <td class="paramname">&#160;</td>
2078 </tr>
2079 <tr>
2080 <td></td>
2081 <td>)</td>
2082 <td></td><td></td>
2083 </tr>
2084 </table>
2085</div><div class="memdoc">
2086
2087</div>
2088</div>
2089<a id="ae2611d5cac758d2eebff6450315aa7df"></a>
2090<h2 class="memtitle"><span class="permalink"><a href="#ae2611d5cac758d2eebff6450315aa7df">&#9670;&nbsp;</a></span>DepthwiseConvolution2d3x3Dilation3x3Test< armnn::DataType::QSymmS16, armnn::DataType::Signed32 >()</h2>
2091
2092<div class="memitem">
2093<div class="memproto">
2094 <table class="memname">
2095 <tr>
2096 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3">DepthwiseConvolution2d3x3Dilation3x3Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &gt; </td>
2097 <td>(</td>
2098 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2099 <td class="paramname">, </td>
2100 </tr>
2101 <tr>
2102 <td class="paramkey"></td>
2103 <td></td>
2104 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2105 <td class="paramname">, </td>
2106 </tr>
2107 <tr>
2108 <td class="paramkey"></td>
2109 <td></td>
2110 <td class="paramtype">bool&#160;</td>
2111 <td class="paramname">, </td>
2112 </tr>
2113 <tr>
2114 <td class="paramkey"></td>
2115 <td></td>
2116 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2117 <td class="paramname">&#160;</td>
2118 </tr>
2119 <tr>
2120 <td></td>
2121 <td>)</td>
2122 <td></td><td></td>
2123 </tr>
2124 </table>
2125</div><div class="memdoc">
2126
2127</div>
2128</div>
2129<a id="a80ee4cde34185af792db65aa40cf5c98"></a>
2130<h2 class="memtitle"><span class="permalink"><a href="#a80ee4cde34185af792db65aa40cf5c98">&#9670;&nbsp;</a></span>DepthwiseConvolution2d3x3DilationTestCommon()</h2>
2131
2132<div class="memitem">
2133<div class="memproto">
2134 <table class="memname">
2135 <tr>
2136 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2d3x3DilationTestCommon </td>
2137 <td>(</td>
2138 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2139 <td class="paramname"><em>workloadFactory</em>, </td>
2140 </tr>
2141 <tr>
2142 <td class="paramkey"></td>
2143 <td></td>
2144 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2145 <td class="paramname"><em>memoryManager</em>, </td>
2146 </tr>
2147 <tr>
2148 <td class="paramkey"></td>
2149 <td></td>
2150 <td class="paramtype">const std::vector&lt; float &gt; &amp;&#160;</td>
2151 <td class="paramname"><em>inputNoQuantizedValues</em>, </td>
2152 </tr>
2153 <tr>
2154 <td class="paramkey"></td>
2155 <td></td>
2156 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;&#160;</td>
2157 <td class="paramname"><em>inputTensorInfo</em>, </td>
2158 </tr>
2159 <tr>
2160 <td class="paramkey"></td>
2161 <td></td>
2162 <td class="paramtype">const std::vector&lt; float &gt; &amp;&#160;</td>
2163 <td class="paramname"><em>kernelNoQuantizedValues</em>, </td>
2164 </tr>
2165 <tr>
2166 <td class="paramkey"></td>
2167 <td></td>
2168 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;&#160;</td>
2169 <td class="paramname"><em>kernelTensorInfo</em>, </td>
2170 </tr>
2171 <tr>
2172 <td class="paramkey"></td>
2173 <td></td>
2174 <td class="paramtype">const std::vector&lt; float &gt; &amp;&#160;</td>
2175 <td class="paramname"><em>outputExpectedNoQuantizedValues</em>, </td>
2176 </tr>
2177 <tr>
2178 <td class="paramkey"></td>
2179 <td></td>
2180 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp;&#160;</td>
2181 <td class="paramname"><em>outputTensorInfo</em>, </td>
2182 </tr>
2183 <tr>
2184 <td class="paramkey"></td>
2185 <td></td>
2186 <td class="paramtype">uint32_t&#160;</td>
2187 <td class="paramname"><em>dilationX</em>, </td>
2188 </tr>
2189 <tr>
2190 <td class="paramkey"></td>
2191 <td></td>
2192 <td class="paramtype">uint32_t&#160;</td>
2193 <td class="paramname"><em>dilationY</em>, </td>
2194 </tr>
2195 <tr>
2196 <td class="paramkey"></td>
2197 <td></td>
2198 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2199 <td class="paramname"><em>layout</em> = <code><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></code>, </td>
2200 </tr>
2201 <tr>
2202 <td class="paramkey"></td>
2203 <td></td>
2204 <td class="paramtype">bool&#160;</td>
2205 <td class="paramname"><em>biasEnabled</em> = <code><a class="el" href="_ref_layer_tests_8cpp.xhtml#af3b727ae5a13ff472892ab8bda2eb1b5">false</a></code>&#160;</td>
2206 </tr>
2207 <tr>
2208 <td></td>
2209 <td>)</td>
2210 <td></td><td></td>
2211 </tr>
2212 </table>
2213</div><div class="memdoc">
2214
2215<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02288">2288</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2216
2217<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::QSymmS16</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00275">TensorInfo::SetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
2218<div class="fragment"><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span>&#160;{</div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>&#160; <span class="keywordtype">float</span> qScale;</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>&#160; int32_t qOffset;</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>&#160; <span class="keywordflow">switch</span> (ArmnnType)</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>&#160; {</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span>&#160; {</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>&#160; qScale = 0.1f;</div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>&#160; qOffset = 128;</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>&#160; }</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>&#160; {</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>&#160; qScale = 0.1f;</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span>&#160; qOffset = 0;</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>&#160; <span class="keywordflow">break</span>;</div><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">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span>&#160; {</div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>&#160; qScale = 0.f;</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>&#160; qOffset = 0;</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>&#160; }</div><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;</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>&#160; kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>&#160; kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><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="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo,</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>&#160; std::vector&lt;T&gt;(QuantizedVector&lt;T&gt;(inputNoQuantizedValues,</div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>&#160; inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>&#160; inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>&#160; <span class="keyword">auto</span> kernel = MakeTensor&lt;T, 4&gt;(kernelTensorInfo,</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>&#160; std::vector&lt;T&gt;(QuantizedVector&lt;T&gt;(kernelNoQuantizedValues,</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>&#160; kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>&#160; kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>&#160; <span class="keyword">auto</span> expectedOutput =</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>&#160; MakeTensor&lt;T, 4&gt;(outputTensorInfo,</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>&#160; std::vector&lt;T&gt;(QuantizedVector&lt;T&gt;(outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>&#160; outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>&#160; outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>&#160;</div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span>&#160; uint32_t padLeft = 0;</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>&#160; uint32_t padTop = 0;</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>&#160; uint32_t padRight = 0;</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>&#160; uint32_t padBottom = 0;</div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>&#160; uint32_t strideX = 1;</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>&#160; uint32_t strideY = 1;</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>&#160;</div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>&#160; workloadFactory,</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>&#160; memoryManager,</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>&#160; input,</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>&#160; kernel,</div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>&#160; GetBias&lt;ArmnnBType&gt;(biasEnabled, qScale * qScale, outputTensorInfo, layout),</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>&#160; expectedOutput,</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>&#160; qScale,</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>&#160; qOffset,</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>&#160; layout,</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>&#160; padLeft,</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>&#160; padTop,</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>&#160; padRight,</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>&#160; padBottom,</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>&#160; strideX,</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>&#160; strideY,</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>&#160; dilationX,</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>&#160; dilationY);</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>&#160;}</div><div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
2219<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
2220<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>
2221<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
2222<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div>
2223</div><!-- fragment -->
2224</div>
2225</div>
2226<a id="abf326cbf49ec19c6272fe7c244b7817c"></a>
2227<h2 class="memtitle"><span class="permalink"><a href="#abf326cbf49ec19c6272fe7c244b7817c">&#9670;&nbsp;</a></span>DepthwiseConvolution2dAsymmetricTest()</h2>
2228
2229<div class="memitem">
2230<div class="memproto">
2231 <table class="memname">
2232 <tr>
2233 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; DepthwiseConvolution2dAsymmetricTest </td>
2234 <td>(</td>
2235 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2236 <td class="paramname"><em>workloadFactory</em>, </td>
2237 </tr>
2238 <tr>
2239 <td class="paramkey"></td>
2240 <td></td>
2241 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2242 <td class="paramname"><em>memoryManager</em>, </td>
2243 </tr>
2244 <tr>
2245 <td class="paramkey"></td>
2246 <td></td>
2247 <td class="paramtype">bool&#160;</td>
2248 <td class="paramname"><em>biasEnabled</em>, </td>
2249 </tr>
2250 <tr>
2251 <td class="paramkey"></td>
2252 <td></td>
2253 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2254 <td class="paramname"><em>layout</em>&#160;</td>
2255 </tr>
2256 <tr>
2257 <td></td>
2258 <td>)</td>
2259 <td></td><td></td>
2260 </tr>
2261 </table>
2262</div><div class="memdoc">
2263
2264<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03254">3254</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2265<div class="fragment"><div class="line"><a name="l03259"></a><span class="lineno"> 3259</span>&#160;{</div><div class="line"><a name="l03260"></a><span class="lineno"> 3260</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dAsymmetricTestCommon&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03261"></a><span class="lineno"> 3261</span>&#160; workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);</div><div class="line"><a name="l03262"></a><span class="lineno"> 3262</span>&#160;}</div></div><!-- fragment -->
2266</div>
2267</div>
2268<a id="a952b4460c66365d89ebb3df940bbd9bd"></a>
2269<h2 class="memtitle"><span class="permalink"><a href="#a952b4460c66365d89ebb3df940bbd9bd">&#9670;&nbsp;</a></span>DepthwiseConvolution2dAsymmetricTestCommon()</h2>
2270
2271<div class="memitem">
2272<div class="memproto">
2273 <table class="memname">
2274 <tr>
2275 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dAsymmetricTestCommon </td>
2276 <td>(</td>
2277 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2278 <td class="paramname"><em>workloadFactory</em>, </td>
2279 </tr>
2280 <tr>
2281 <td class="paramkey"></td>
2282 <td></td>
2283 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2284 <td class="paramname"><em>memoryManager</em>, </td>
2285 </tr>
2286 <tr>
2287 <td class="paramkey"></td>
2288 <td></td>
2289 <td class="paramtype">float&#160;</td>
2290 <td class="paramname"><em>qScale</em>, </td>
2291 </tr>
2292 <tr>
2293 <td class="paramkey"></td>
2294 <td></td>
2295 <td class="paramtype">int32_t&#160;</td>
2296 <td class="paramname"><em>qOffset</em>, </td>
2297 </tr>
2298 <tr>
2299 <td class="paramkey"></td>
2300 <td></td>
2301 <td class="paramtype">bool&#160;</td>
2302 <td class="paramname"><em>biasEnabled</em>, </td>
2303 </tr>
2304 <tr>
2305 <td class="paramkey"></td>
2306 <td></td>
2307 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2308 <td class="paramname"><em>layout</em>&#160;</td>
2309 </tr>
2310 <tr>
2311 <td></td>
2312 <td>)</td>
2313 <td></td><td></td>
2314 </tr>
2315 </table>
2316</div><div class="memdoc">
2317
2318<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02047">2047</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2319<div class="fragment"><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>&#160;{</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>&#160; <span class="comment">// Use a single-batch 2-channel 5x5 image as input.</span></div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 2, 5, 5 }, ArmnnType);</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>&#160; 0, 1, 2, 3, 4,</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>&#160; 5, 6, 7, 8, 9,</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>&#160; 10, 11, 12, 13, 14,</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>&#160; 15, 16, 17, 18, 19,</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>&#160; 20, 21, 22, 23, 24,</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>&#160;</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>&#160; 25, 26, 27, 28, 29,</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>&#160; 30, 31, 32, 33, 34,</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>&#160; 35, 36, 37, 38, 39,</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>&#160; 40, 41, 42, 43, 44,</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>&#160; 45, 46, 47, 48, 49</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>&#160; },</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>&#160; inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>&#160; inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>&#160;</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>&#160; <span class="comment">// Use a depth multiplier of 1 on a 2-channel 4x4 kernel.</span></div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType);</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>&#160; <span class="keyword">auto</span> kernel = MakeTensor&lt;T, 4&gt;(kernelTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>&#160; 32, 31, 30, 29,</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>&#160; 28, 27, 26, 25,</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>&#160; 24, 23, 22, 21,</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>&#160; 20, 19, 18, 17,</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>&#160;</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>&#160; 16, 15, 14, 13,</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>&#160; 12, 11, 10, 9,</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>&#160; 8, 7, 6, 5,</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>&#160; 4, 3, 2, 1</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>&#160; },</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>&#160; kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>&#160; kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>&#160;</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>&#160; <span class="comment">// Expected output is 1 batch of a 2-channel 5x5 image.</span></div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>&#160; <span class="comment">// Calculated using the python tensorflow library with strideX=1, strideY=1.</span></div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2, 5, 5 }, ArmnnType);</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>&#160; 1062, 1580, 1850, 1530, 1117,</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>&#160; 2140, 3108, 3500, 2842, 2042,</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>&#160; 3580, 5068, 5460, 4342, 3062,</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>&#160; 3618, 5072, 5390, 4248, 2971,</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>&#160; 3074, 4282, 4510, 3533, 2457,</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>&#160;</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>&#160; 1550, 2284, 2362, 1955, 1428,</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>&#160; 2910, 4206, 4342, 3528, 2536,</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>&#160; 3390, 4886, 5022, 4068, 2916,</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>&#160; 3566, 5056, 5182, 4133, 2922,</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>&#160; 3100, 4352, 4452, 3517, 2465</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>&#160; },</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>&#160; outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>&#160; outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>&#160;</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dAsymmetricTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>&#160; workloadFactory,</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>&#160; memoryManager,</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>&#160; input,</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>&#160; kernel,</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>&#160; GetBias2&lt;ArmnnBType&gt;(biasEnabled, qScale * qScale),</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>&#160; expectedOutput,</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>&#160; qScale,</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>&#160; qOffset,</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>&#160; layout,</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>&#160; 1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>&#160; 1, <span class="comment">// Padding top.</span></div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>&#160; 2, <span class="comment">// Padding right.</span></div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>&#160; 2, <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>&#160; 1, <span class="comment">// strideX</span></div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>&#160; 1); <span class="comment">// strideY</span></div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</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>
2320</div><!-- fragment -->
2321</div>
2322</div>
2323<a id="aa405363108e52032fb1e23c3f5a03a57"></a>
2324<h2 class="memtitle"><span class="permalink"><a href="#aa405363108e52032fb1e23c3f5a03a57">&#9670;&nbsp;</a></span>DepthwiseConvolution2dAsymmetricTestImpl()</h2>
2325
2326<div class="memitem">
2327<div class="memproto">
2328 <table class="memname">
2329 <tr>
2330 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dAsymmetricTestImpl </td>
2331 <td>(</td>
2332 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2333 <td class="paramname"><em>workloadFactory</em>, </td>
2334 </tr>
2335 <tr>
2336 <td class="paramkey"></td>
2337 <td></td>
2338 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2339 <td class="paramname"><em>memoryManager</em>, </td>
2340 </tr>
2341 <tr>
2342 <td class="paramkey"></td>
2343 <td></td>
2344 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
2345 <td class="paramname"><em>input</em>, </td>
2346 </tr>
2347 <tr>
2348 <td class="paramkey"></td>
2349 <td></td>
2350 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
2351 <td class="paramname"><em>kernel</em>, </td>
2352 </tr>
2353 <tr>
2354 <td class="paramkey"></td>
2355 <td></td>
2356 <td class="paramtype">const boost::multi_array&lt; B, 1 &gt; &amp;&#160;</td>
2357 <td class="paramname"><em>bias</em>, </td>
2358 </tr>
2359 <tr>
2360 <td class="paramkey"></td>
2361 <td></td>
2362 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
2363 <td class="paramname"><em>outputExpected</em>, </td>
2364 </tr>
2365 <tr>
2366 <td class="paramkey"></td>
2367 <td></td>
2368 <td class="paramtype">float&#160;</td>
2369 <td class="paramname"><em>qScale</em>, </td>
2370 </tr>
2371 <tr>
2372 <td class="paramkey"></td>
2373 <td></td>
2374 <td class="paramtype">int32_t&#160;</td>
2375 <td class="paramname"><em>qOffset</em>, </td>
2376 </tr>
2377 <tr>
2378 <td class="paramkey"></td>
2379 <td></td>
2380 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2381 <td class="paramname"><em>layout</em>, </td>
2382 </tr>
2383 <tr>
2384 <td class="paramkey"></td>
2385 <td></td>
2386 <td class="paramtype">uint32_t&#160;</td>
2387 <td class="paramname"><em>padLeft</em> = <code>0</code>, </td>
2388 </tr>
2389 <tr>
2390 <td class="paramkey"></td>
2391 <td></td>
2392 <td class="paramtype">uint32_t&#160;</td>
2393 <td class="paramname"><em>padTop</em> = <code>0</code>, </td>
2394 </tr>
2395 <tr>
2396 <td class="paramkey"></td>
2397 <td></td>
2398 <td class="paramtype">uint32_t&#160;</td>
2399 <td class="paramname"><em>padRight</em> = <code>0</code>, </td>
2400 </tr>
2401 <tr>
2402 <td class="paramkey"></td>
2403 <td></td>
2404 <td class="paramtype">uint32_t&#160;</td>
2405 <td class="paramname"><em>padBottom</em> = <code>0</code>, </td>
2406 </tr>
2407 <tr>
2408 <td class="paramkey"></td>
2409 <td></td>
2410 <td class="paramtype">uint32_t&#160;</td>
2411 <td class="paramname"><em>strideX</em> = <code>1</code>, </td>
2412 </tr>
2413 <tr>
2414 <td class="paramkey"></td>
2415 <td></td>
2416 <td class="paramtype">uint32_t&#160;</td>
2417 <td class="paramname"><em>strideY</em> = <code>1</code>&#160;</td>
2418 </tr>
2419 <tr>
2420 <td></td>
2421 <td>)</td>
2422 <td></td><td></td>
2423 </tr>
2424 </table>
2425</div><div class="memdoc">
2426
2427<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01381">1381</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2428
2429<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00169">ApplyBias()</a>, <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#l01177">IWorkloadFactory::CreateDepthwiseConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00264">TensorInfo::GetQuantizationOffset()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00247">TensorInfo::GetQuantizationScale()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_tensor_utils_8cpp_source.xhtml#l00038">armnnUtils::GetTensorInfo()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00192">DepthwiseConvolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00490">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00492">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00480">DepthwiseConvolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00474">DepthwiseConvolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00476">DepthwiseConvolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00478">DepthwiseConvolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00482">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00484">DepthwiseConvolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00191">DepthwiseConvolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, <a class="el" href="_numeric_cast_8hpp_source.xhtml#l00033">armnn::numeric_cast()</a>, <a class="el" href="_permute_8cpp_source.xhtml#l00121">armnnUtils::Permute()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00275">TensorInfo::SetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
2430<div class="fragment"><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>&#160;{</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[0]);</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[1]);</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[2]);</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[3]);</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChanMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[0]);</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[1]);</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[2]);</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[3]);</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[0]);</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[1]);</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[2]);</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[3]);</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>&#160;</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>&#160; <span class="comment">// If a bias is used, its size must equal the number of output channels.</span></div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>&#160; <span class="keywordtype">bool</span> biasEnabled = bias.size() &gt; 0;</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>&#160; BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>&#160;</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>&#160; <span class="comment">// Creates the tensors.</span></div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(bias.size())}, ArmnnBType);</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; 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kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>&#160; kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>&#160; biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>&#160; biasDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>&#160; }</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>&#160;</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>&#160; <span class="comment">// Construct the input data.</span></div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>&#160; std::vector&lt;T&gt; inputData;</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>&#160; inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>&#160;</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>&#160; <span class="comment">// At this point if we require it permute the input data</span></div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>&#160; {</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>&#160; std::vector&lt;T&gt; tmp(inputData.size());</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>&#160; inputData = tmp;</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;</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>&#160; <span class="keyword">auto</span> batchedInput = MakeTensor&lt;T, 4&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>&#160;</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>&#160; <span class="comment">// Construct the output data, with bias applied, as appropriate.</span></div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>&#160; std::vector&lt;T&gt; outputData;</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>&#160; outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>&#160; <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>&#160; {</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>&#160; std::vector&lt;T&gt; biasV;</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>&#160; biasV.assign(bias.data(), bias.data() + outputChannels);</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>&#160; <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputData, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>&#160; biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>&#160; outputWidth, outputHeight);</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>&#160; }</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>&#160;</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>&#160;</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>&#160; <span class="comment">// At this point if we require it permute the expected output</span></div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><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; std::vector&lt;T&gt; tmp(outputData.size());</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>&#160; outputData = tmp;</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>&#160; }</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; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, outputData);</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; std::unique_ptr&lt;armnn::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="l01476"></a><span class="lineno"> 1476</span>&#160; std::unique_ptr&lt;armnn::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="l01477"></a><span class="lineno"> 1477</span>&#160;</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>&#160;</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</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; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>&#160; <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>&#160; {</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, &amp;bias[0]);</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>&#160; }</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>&#160;</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor; <span class="comment">// Still set this whether or not bias is enabled - it can be a source of bugs.</span></div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>&#160;</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>&#160; 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outputHandle-&gt;Allocate();</div><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="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;batchedInput[0][0][0][0]);</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; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>&#160;</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>&#160;</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div>
2431<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>
2432<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div>
2433<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa1f4ce02e0904dc8cf1b7f42bc34d346"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a></div><div class="ttdeci">void ApplyBias(std::vector&lt; T &gt; &amp;v, float vScale, int32_t vOffset, const std::vector&lt; B &gt; &amp;bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00169">Conv2dTestImpl.cpp:169</a></div></div>
2434<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div>
2435<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>
2436<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::DepthwiseConvolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00192">WorkloadData.hpp:192</a></div></div>
2437<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div>
2438<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>
2439<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::DepthwiseConvolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00191">WorkloadData.hpp:191</a></div></div>
2440<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div>
2441<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</a></div></div>
2442<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div>
2443<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div>
2444<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</a></div></div>
2445<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>
2446<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div>
2447<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
2448<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>
2449<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>
2450<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>
2451<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
2452<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div>
2453<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
2454<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div>
2455<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>
2456<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>
2457<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div>
2458<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_accb9759dfd2880efe0f8d2705ddee448"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">armnn::IWorkloadFactory::CreateDepthwiseConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01177">WorkloadFactory.cpp:1177</a></div></div>
2459<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00183">WorkloadData.hpp:183</a></div></div>
2460<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
2461<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>
2462<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div>
2463</div><!-- fragment -->
2464</div>
2465</div>
2466<a id="a74346a72d64f7fa3463473424c3098ab"></a>
2467<h2 class="memtitle"><span class="permalink"><a href="#a74346a72d64f7fa3463473424c3098ab">&#9670;&nbsp;</a></span>DepthwiseConvolution2dDepthMul1Int16Test()</h2>
2468
2469<div class="memitem">
2470<div class="memproto">
2471 <table class="memname">
2472 <tr>
2473 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;int16_t, 4&gt; DepthwiseConvolution2dDepthMul1Int16Test </td>
2474 <td>(</td>
2475 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2476 <td class="paramname"><em>workloadFactory</em>, </td>
2477 </tr>
2478 <tr>
2479 <td class="paramkey"></td>
2480 <td></td>
2481 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2482 <td class="paramname"><em>memoryManager</em>, </td>
2483 </tr>
2484 <tr>
2485 <td class="paramkey"></td>
2486 <td></td>
2487 <td class="paramtype">bool&#160;</td>
2488 <td class="paramname"><em>biasEnabled</em>, </td>
2489 </tr>
2490 <tr>
2491 <td class="paramkey"></td>
2492 <td></td>
2493 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2494 <td class="paramname"><em>layout</em>&#160;</td>
2495 </tr>
2496 <tr>
2497 <td></td>
2498 <td>)</td>
2499 <td></td><td></td>
2500 </tr>
2501 </table>
2502</div><div class="memdoc">
2503
2504<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03306">3306</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2505<div class="fragment"><div class="line"><a name="l03311"></a><span class="lineno"> 3311</span>&#160;{</div><div class="line"><a name="l03312"></a><span class="lineno"> 3312</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dDepthMul1TestImpl&lt;armnn::DataType::QSymmS16, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03313"></a><span class="lineno"> 3313</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03314"></a><span class="lineno"> 3314</span>&#160;}</div></div><!-- fragment -->
2506</div>
2507</div>
2508<a id="a8b32d950a40903f502f5e1ec0dcab0bd"></a>
2509<h2 class="memtitle"><span class="permalink"><a href="#a8b32d950a40903f502f5e1ec0dcab0bd">&#9670;&nbsp;</a></span>DepthwiseConvolution2dDepthMul1Test()</h2>
2510
2511<div class="memitem">
2512<div class="memproto">
2513 <table class="memname">
2514 <tr>
2515 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; DepthwiseConvolution2dDepthMul1Test </td>
2516 <td>(</td>
2517 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2518 <td class="paramname"><em>workloadFactory</em>, </td>
2519 </tr>
2520 <tr>
2521 <td class="paramkey"></td>
2522 <td></td>
2523 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2524 <td class="paramname"><em>memoryManager</em>, </td>
2525 </tr>
2526 <tr>
2527 <td class="paramkey"></td>
2528 <td></td>
2529 <td class="paramtype">bool&#160;</td>
2530 <td class="paramname"><em>biasEnabled</em>, </td>
2531 </tr>
2532 <tr>
2533 <td class="paramkey"></td>
2534 <td></td>
2535 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2536 <td class="paramname"><em>layout</em>&#160;</td>
2537 </tr>
2538 <tr>
2539 <td></td>
2540 <td>)</td>
2541 <td></td><td></td>
2542 </tr>
2543 </table>
2544</div><div class="memdoc">
2545
2546<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03212">3212</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2547<div class="fragment"><div class="line"><a name="l03217"></a><span class="lineno"> 3217</span>&#160;{</div><div class="line"><a name="l03218"></a><span class="lineno"> 3218</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dDepthMul1TestImpl&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03219"></a><span class="lineno"> 3219</span>&#160; workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);</div><div class="line"><a name="l03220"></a><span class="lineno"> 3220</span>&#160;}</div></div><!-- fragment -->
2548</div>
2549</div>
2550<a id="a01eae690cbfa5359968f4b8ee13b8814"></a>
2551<h2 class="memtitle"><span class="permalink"><a href="#a01eae690cbfa5359968f4b8ee13b8814">&#9670;&nbsp;</a></span>DepthwiseConvolution2dDepthMul1TestImpl()</h2>
2552
2553<div class="memitem">
2554<div class="memproto">
2555 <table class="memname">
2556 <tr>
2557 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dDepthMul1TestImpl </td>
2558 <td>(</td>
2559 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2560 <td class="paramname"><em>workloadFactory</em>, </td>
2561 </tr>
2562 <tr>
2563 <td class="paramkey"></td>
2564 <td></td>
2565 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2566 <td class="paramname"><em>memoryManager</em>, </td>
2567 </tr>
2568 <tr>
2569 <td class="paramkey"></td>
2570 <td></td>
2571 <td class="paramtype">float&#160;</td>
2572 <td class="paramname"><em>qScale</em>, </td>
2573 </tr>
2574 <tr>
2575 <td class="paramkey"></td>
2576 <td></td>
2577 <td class="paramtype">int32_t&#160;</td>
2578 <td class="paramname"><em>qOffset</em>, </td>
2579 </tr>
2580 <tr>
2581 <td class="paramkey"></td>
2582 <td></td>
2583 <td class="paramtype">bool&#160;</td>
2584 <td class="paramname"><em>biasEnabled</em>, </td>
2585 </tr>
2586 <tr>
2587 <td class="paramkey"></td>
2588 <td></td>
2589 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2590 <td class="paramname"><em>layout</em>&#160;</td>
2591 </tr>
2592 <tr>
2593 <td></td>
2594 <td>)</td>
2595 <td></td><td></td>
2596 </tr>
2597 </table>
2598</div><div class="memdoc">
2599
2600<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01518">1518</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2601
2602<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00169">ApplyBias()</a>, <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#l01177">IWorkloadFactory::CreateDepthwiseConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00264">TensorInfo::GetQuantizationOffset()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00247">TensorInfo::GetQuantizationScale()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_tensor_utils_8cpp_source.xhtml#l00038">armnnUtils::GetTensorInfo()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00192">DepthwiseConvolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00490">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00492">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00480">DepthwiseConvolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00474">DepthwiseConvolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00476">DepthwiseConvolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00478">DepthwiseConvolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00482">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00484">DepthwiseConvolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00191">DepthwiseConvolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, <a class="el" href="_permute_8cpp_source.xhtml#l00121">armnnUtils::Permute()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00275">TensorInfo::SetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
2603<div class="fragment"><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>&#160;{</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>&#160; <span class="keyword">using</span> <a class="code" href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">B</a> = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType&lt;ArmnnBType&gt;</a>;</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>&#160;</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 3;</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3;</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 2;</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 1;</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>&#160;</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 3;</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = inputChannels;</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelDepthMultiplier = 1;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 1;</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 1;</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = kernelChannels;</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>&#160;</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelDepthMultiplier, kernelChannels, kernelHeight, kernelWidth},</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>&#160; ArmnnType);</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({ outputChannels }, ArmnnBType);</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>&#160;</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>&#160; <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>&#160; <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>&#160; {</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>&#160; kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>&#160; kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>&#160; biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>&#160; biasDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>&#160; }</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>&#160; std::vector&lt;T&gt; inputData = std::vector&lt;T&gt;(</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>&#160; 1.f, 2.f, 1.f,</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>&#160; 2.f, 1.f, 2.f,</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>&#160; 1.f, 2.f, 1.f,</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; 1.f, 2.f, 1.f,</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>&#160; 2.f, 1.f, 2.f,</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>&#160; 1.f, 2.f, 1.f,</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>&#160; },</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</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; <span class="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>&#160; {</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>&#160; std::vector&lt;T&gt; tmp(inputData.size());</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>&#160; inputData = tmp;</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>&#160; }</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>&#160;</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>&#160; std::vector&lt;B&gt; biasV(QuantizedVector&lt;B&gt;({ 0, 2 },</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>&#160; biasDesc.GetQuantizationScale(),</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>&#160; biasDesc.GetQuantizationOffset()));</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>&#160;</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>&#160; <span class="keyword">auto</span> bias = MakeTensor&lt;B, 1&gt;(biasDesc, biasV);</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>&#160;</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>&#160; std::vector&lt;T&gt; kernelData = std::vector&lt;T&gt;(</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>&#160; 1.f, 0.f, 1.f,</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>&#160; 0.f, 0.f, 0.f,</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>&#160; -1.f, 0.f, -1.f,</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>&#160;</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>&#160; 1.f, 0.f, 1.f,</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>&#160; 0.f, 0.f, 0.f,</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>&#160; -1.f, 0.f, -1.f,</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>&#160; },</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>&#160; kernelDesc.GetQuantizationScale(),</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>&#160; kernelDesc.GetQuantizationOffset()));</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>&#160;</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>&#160; <span class="keyword">auto</span> kernel = MakeTensor&lt;T, 4&gt;(kernelDesc, kernelData);</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>&#160;</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>&#160; <span class="comment">// Manually calculated.</span></div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>&#160; std::vector&lt;T&gt; outputImage(</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>&#160; QuantizedVector&lt;T&gt;({ 0.f, 0.f },</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>())</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>&#160; );</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>&#160;</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>&#160; <span class="comment">// Optionally apply bias to output image.</span></div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</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; <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputImage, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>&#160; biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>&#160; outputWidth, outputHeight);</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>&#160; }</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>&#160;</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>&#160; {</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>&#160; std::vector&lt;T&gt; tmp(outputImage.size());</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputImage.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>&#160; outputImage = tmp;</div><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;</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>&#160; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, outputImage);</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>&#160;</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>&#160; std::unique_ptr&lt;armnn::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="l01633"></a><span class="lineno"> 1633</span>&#160; std::unique_ptr&lt;armnn::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="l01634"></a><span class="lineno"> 1634</span>&#160;</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>&#160;</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, &amp;bias[0]);</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; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>&#160;</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor; <span class="comment">// Still set this whether or not bias is enabled.</span></div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>&#160;</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>&#160; outputHandle-&gt;Allocate();</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; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</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; ExecuteWorkload(*workload, memoryManager);</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="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</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; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div>
2604<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>
2605<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div>
2606<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa1f4ce02e0904dc8cf1b7f42bc34d346"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a></div><div class="ttdeci">void ApplyBias(std::vector&lt; T &gt; &amp;v, float vScale, int32_t vOffset, const std::vector&lt; B &gt; &amp;bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00169">Conv2dTestImpl.cpp:169</a></div></div>
2607<div class="ttc" id="_inference_test_image_8hpp_xhtml_a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571"><div class="ttname"><a href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">ImageChannel::B</a></div></div>
2608<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div>
2609<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>
2610<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::DepthwiseConvolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00192">WorkloadData.hpp:192</a></div></div>
2611<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div>
2612<div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl&lt; DT &gt;::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00073">ResolveType.hpp:73</a></div></div>
2613<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>
2614<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::DepthwiseConvolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00191">WorkloadData.hpp:191</a></div></div>
2615<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div>
2616<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</a></div></div>
2617<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div>
2618<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div>
2619<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</a></div></div>
2620<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>
2621<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
2622<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>
2623<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>
2624<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>
2625<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
2626<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div>
2627<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
2628<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div>
2629<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>
2630<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>
2631<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div>
2632<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_accb9759dfd2880efe0f8d2705ddee448"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">armnn::IWorkloadFactory::CreateDepthwiseConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01177">WorkloadFactory.cpp:1177</a></div></div>
2633<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00183">WorkloadData.hpp:183</a></div></div>
2634<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
2635<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>
2636<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div>
2637</div><!-- fragment -->
2638</div>
2639</div>
2640<a id="ae797be34b659db2afe183f0c762fb9b7"></a>
2641<h2 class="memtitle"><span class="permalink"><a href="#ae797be34b659db2afe183f0c762fb9b7">&#9670;&nbsp;</a></span>DepthwiseConvolution2dDepthMul1Uint8Test()</h2>
2642
2643<div class="memitem">
2644<div class="memproto">
2645 <table class="memname">
2646 <tr>
2647 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; DepthwiseConvolution2dDepthMul1Uint8Test </td>
2648 <td>(</td>
2649 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2650 <td class="paramname"><em>workloadFactory</em>, </td>
2651 </tr>
2652 <tr>
2653 <td class="paramkey"></td>
2654 <td></td>
2655 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2656 <td class="paramname"><em>memoryManager</em>, </td>
2657 </tr>
2658 <tr>
2659 <td class="paramkey"></td>
2660 <td></td>
2661 <td class="paramtype">bool&#160;</td>
2662 <td class="paramname"><em>biasEnabled</em>, </td>
2663 </tr>
2664 <tr>
2665 <td class="paramkey"></td>
2666 <td></td>
2667 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2668 <td class="paramname"><em>layout</em>&#160;</td>
2669 </tr>
2670 <tr>
2671 <td></td>
2672 <td>)</td>
2673 <td></td><td></td>
2674 </tr>
2675 </table>
2676</div><div class="memdoc">
2677
2678<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03274">3274</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2679<div class="fragment"><div class="line"><a name="l03279"></a><span class="lineno"> 3279</span>&#160;{</div><div class="line"><a name="l03280"></a><span class="lineno"> 3280</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dDepthMul1TestImpl&lt;armnn::DataType::QAsymmU8, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03281"></a><span class="lineno"> 3281</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03282"></a><span class="lineno"> 3282</span>&#160;}</div></div><!-- fragment -->
2680</div>
2681</div>
2682<a id="ab020b4a99bf905b61a1c5e03332b63a6"></a>
2683<h2 class="memtitle"><span class="permalink"><a href="#ab020b4a99bf905b61a1c5e03332b63a6">&#9670;&nbsp;</a></span>DepthwiseConvolution2dDepthMul64Test()</h2>
2684
2685<div class="memitem">
2686<div class="memproto">
2687 <table class="memname">
2688 <tr>
2689 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; DepthwiseConvolution2dDepthMul64Test </td>
2690 <td>(</td>
2691 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2692 <td class="paramname"><em>workloadFactory</em>, </td>
2693 </tr>
2694 <tr>
2695 <td class="paramkey"></td>
2696 <td></td>
2697 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2698 <td class="paramname"><em>memoryManager</em>&#160;</td>
2699 </tr>
2700 <tr>
2701 <td></td>
2702 <td>)</td>
2703 <td></td><td></td>
2704 </tr>
2705 </table>
2706</div><div class="memdoc">
2707
2708<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03222">3222</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2709
2710<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, and <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</a>.</p>
2711<div class="fragment"><div class="line"><a name="l03225"></a><span class="lineno"> 3225</span>&#160;{</div><div class="line"><a name="l03226"></a><span class="lineno"> 3226</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03227"></a><span class="lineno"> 3227</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;float, 4&gt;(inputTensorInfo, { 1.f, 2.f, 3.f, 4.f });</div><div class="line"><a name="l03228"></a><span class="lineno"> 3228</span>&#160;</div><div class="line"><a name="l03229"></a><span class="lineno"> 3229</span>&#160; std::vector&lt;float&gt; kernelData;</div><div class="line"><a name="l03230"></a><span class="lineno"> 3230</span>&#160; std::vector&lt;float&gt; singleDepthKernel{ 1.f, -1.f, -1.f, 1.f };</div><div class="line"><a name="l03231"></a><span class="lineno"> 3231</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; 64; ++i)</div><div class="line"><a name="l03232"></a><span class="lineno"> 3232</span>&#160; {</div><div class="line"><a name="l03233"></a><span class="lineno"> 3233</span>&#160; kernelData.insert(kernelData.end(), singleDepthKernel.begin(), singleDepthKernel.end());</div><div class="line"><a name="l03234"></a><span class="lineno"> 3234</span>&#160; }</div><div class="line"><a name="l03235"></a><span class="lineno"> 3235</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 64, 1, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03236"></a><span class="lineno"> 3236</span>&#160; <span class="keyword">auto</span> kernel = MakeTensor&lt;float, 4&gt;(kernelTensorInfo, kernelData);</div><div class="line"><a name="l03237"></a><span class="lineno"> 3237</span>&#160;</div><div class="line"><a name="l03238"></a><span class="lineno"> 3238</span>&#160; std::vector&lt;float&gt; expectedOutputData(64, 0.f);</div><div class="line"><a name="l03239"></a><span class="lineno"> 3239</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 64, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03240"></a><span class="lineno"> 3240</span>&#160; <span class="keyword">auto</span> expectedOutput = MakeTensor&lt;float, 4&gt;(outputTensorInfo, expectedOutputData);</div><div class="line"><a name="l03241"></a><span class="lineno"> 3241</span>&#160;</div><div class="line"><a name="l03242"></a><span class="lineno"> 3242</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03243"></a><span class="lineno"> 3243</span>&#160; workloadFactory,</div><div class="line"><a name="l03244"></a><span class="lineno"> 3244</span>&#160; memoryManager,</div><div class="line"><a name="l03245"></a><span class="lineno"> 3245</span>&#160; input,</div><div class="line"><a name="l03246"></a><span class="lineno"> 3246</span>&#160; kernel,</div><div class="line"><a name="l03247"></a><span class="lineno"> 3247</span>&#160; boost::multi_array&lt;float, 1&gt;(),</div><div class="line"><a name="l03248"></a><span class="lineno"> 3248</span>&#160; expectedOutput,</div><div class="line"><a name="l03249"></a><span class="lineno"> 3249</span>&#160; 0.f,</div><div class="line"><a name="l03250"></a><span class="lineno"> 3250</span>&#160; 0,</div><div class="line"><a name="l03251"></a><span class="lineno"> 3251</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l03252"></a><span class="lineno"> 3252</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>
2712<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
2713<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
2714</div><!-- fragment -->
2715</div>
2716</div>
2717<a id="a0cccb5cffee89004bc8d9fb309ed6636"></a>
2718<h2 class="memtitle"><span class="permalink"><a href="#a0cccb5cffee89004bc8d9fb309ed6636">&#9670;&nbsp;</a></span>DepthwiseConvolution2dDepthNhwcTest()</h2>
2719
2720<div class="memitem">
2721<div class="memproto">
2722 <table class="memname">
2723 <tr>
2724 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; DepthwiseConvolution2dDepthNhwcTest </td>
2725 <td>(</td>
2726 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2727 <td class="paramname"><em>workloadFactory</em>, </td>
2728 </tr>
2729 <tr>
2730 <td class="paramkey"></td>
2731 <td></td>
2732 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2733 <td class="paramname"><em>memoryManager</em>, </td>
2734 </tr>
2735 <tr>
2736 <td class="paramkey"></td>
2737 <td></td>
2738 <td class="paramtype">bool&#160;</td>
2739 <td class="paramname"><em>biasEnabled</em>&#160;</td>
2740 </tr>
2741 <tr>
2742 <td></td>
2743 <td>)</td>
2744 <td></td><td></td>
2745 </tr>
2746 </table>
2747</div><div class="memdoc">
2748
2749<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03203">3203</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2750<div class="fragment"><div class="line"><a name="l03207"></a><span class="lineno"> 3207</span>&#160;{</div><div class="line"><a name="l03208"></a><span class="lineno"> 3208</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dNhwcTestCommon&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03209"></a><span class="lineno"> 3209</span>&#160; workloadFactory, memoryManager, 0.0f, 0, biasEnabled);</div><div class="line"><a name="l03210"></a><span class="lineno"> 3210</span>&#160;}</div></div><!-- fragment -->
2751</div>
2752</div>
2753<a id="a2ae97c2dd6621f4972c571cf1ec2a005"></a>
2754<h2 class="memtitle"><span class="permalink"><a href="#a2ae97c2dd6621f4972c571cf1ec2a005">&#9670;&nbsp;</a></span>DepthwiseConvolution2dInt16Test()</h2>
2755
2756<div class="memitem">
2757<div class="memproto">
2758 <table class="memname">
2759 <tr>
2760 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;int16_t, 4&gt; DepthwiseConvolution2dInt16Test </td>
2761 <td>(</td>
2762 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2763 <td class="paramname"><em>workloadFactory</em>, </td>
2764 </tr>
2765 <tr>
2766 <td class="paramkey"></td>
2767 <td></td>
2768 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2769 <td class="paramname"><em>memoryManager</em>, </td>
2770 </tr>
2771 <tr>
2772 <td class="paramkey"></td>
2773 <td></td>
2774 <td class="paramtype">bool&#160;</td>
2775 <td class="paramname"><em>biasEnabled</em>, </td>
2776 </tr>
2777 <tr>
2778 <td class="paramkey"></td>
2779 <td></td>
2780 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2781 <td class="paramname"><em>layout</em>&#160;</td>
2782 </tr>
2783 <tr>
2784 <td></td>
2785 <td>)</td>
2786 <td></td><td></td>
2787 </tr>
2788 </table>
2789</div><div class="memdoc">
2790
2791<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03296">3296</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2792<div class="fragment"><div class="line"><a name="l03301"></a><span class="lineno"> 3301</span>&#160;{</div><div class="line"><a name="l03302"></a><span class="lineno"> 3302</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl&lt;armnn::DataType::QSymmS16, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03303"></a><span class="lineno"> 3303</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03304"></a><span class="lineno"> 3304</span>&#160;}</div></div><!-- fragment -->
2793</div>
2794</div>
2795<a id="aaed50a372a6b59b20e38469856a3ce6b"></a>
2796<h2 class="memtitle"><span class="permalink"><a href="#aaed50a372a6b59b20e38469856a3ce6b">&#9670;&nbsp;</a></span>DepthwiseConvolution2dMult2Test()</h2>
2797
2798<div class="memitem">
2799<div class="memproto">
2800 <table class="memname">
2801 <tr>
2802 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dMult2Test </td>
2803 <td>(</td>
2804 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2805 <td class="paramname"><em>workloadFactory</em>, </td>
2806 </tr>
2807 <tr>
2808 <td class="paramkey"></td>
2809 <td></td>
2810 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2811 <td class="paramname"><em>memoryManager</em>, </td>
2812 </tr>
2813 <tr>
2814 <td class="paramkey"></td>
2815 <td></td>
2816 <td class="paramtype">bool&#160;</td>
2817 <td class="paramname"><em>biasEnabled</em>, </td>
2818 </tr>
2819 <tr>
2820 <td class="paramkey"></td>
2821 <td></td>
2822 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2823 <td class="paramname"><em>layout</em>&#160;</td>
2824 </tr>
2825 <tr>
2826 <td></td>
2827 <td>)</td>
2828 <td></td><td></td>
2829 </tr>
2830 </table>
2831</div><div class="memdoc">
2832
2833<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02600">2600</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2834<div class="fragment"><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>&#160;{</div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>&#160; std::vector&lt;float&gt; inputNoQuantizedValues =</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>&#160; {</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>&#160; 10.0, 10.0, 10.0,</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>&#160; 10.0, 10.0, 10.0,</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>&#160; 10.0, 10.0, 10.0,</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>&#160;</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>&#160; 21.0, 22.0, 23.0,</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>&#160; 24.0, 25.0, 26.0,</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>&#160; 27.0, 28.0, 29.0</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>&#160; };</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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 2, 2, 2, 2}, ArmnnType);</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; std::vector&lt;float&gt; kernelNoQuantizedValues =</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>&#160; {</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>&#160; 0.25f, 0.25f,</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>&#160; 0.25f, 0.25f,</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>&#160;</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>&#160; 0.2f , 0.0f,</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>&#160; 0.0f , 0.0f,</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>&#160;</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>&#160; 0.0f , 0.0f,</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>&#160; 0.0f , 0.1f,</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>&#160;</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>&#160; 0.0f , 0.3f,</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>&#160; 0.0f , 0.0f</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>&#160;</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>&#160; };</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>&#160;</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 4, 2, 2}, ArmnnType);</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>&#160; std::vector&lt;float&gt; outputExpectedNoQuantizedValues =</div><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; 10.f, 10.f,</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>&#160; 10.f, 10.f,</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>&#160;</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>&#160; 1.f, 1.f,</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; 4.2000003f, 4.4f,</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>&#160; 4.8f, 5.f,</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>&#160;</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>&#160; 6.6000004f, 6.9f,</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>&#160; 7.5000005f, 7.8f</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>&#160; };</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>&#160;</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="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>&#160; workloadFactory,</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>&#160; memoryManager,</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>&#160; inputNoQuantizedValues,</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>&#160; inputTensorInfo,</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>&#160; kernelNoQuantizedValues,</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>&#160; kernelTensorInfo,</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>&#160; outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>&#160; outputTensorInfo,</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>&#160; 1,</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>&#160; 1,</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>&#160; layout,</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>&#160; biasEnabled);</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</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>
2835</div><!-- fragment -->
2836</div>
2837</div>
2838<a id="aebd0b859b0bac0ebaf2812e7991f268d"></a>
2839<h2 class="memtitle"><span class="permalink"><a href="#aebd0b859b0bac0ebaf2812e7991f268d">&#9670;&nbsp;</a></span>DepthwiseConvolution2dMult2Test< armnn::DataType::BFloat16, armnn::DataType::BFloat16 >()</h2>
2840
2841<div class="memitem">
2842<div class="memproto">
2843 <table class="memname">
2844 <tr>
2845 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#aaed50a372a6b59b20e38469856a3ce6b">DepthwiseConvolution2dMult2Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt; </td>
2846 <td>(</td>
2847 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2848 <td class="paramname"><em>workloadFactory</em>, </td>
2849 </tr>
2850 <tr>
2851 <td class="paramkey"></td>
2852 <td></td>
2853 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2854 <td class="paramname"><em>memoryManager</em>, </td>
2855 </tr>
2856 <tr>
2857 <td class="paramkey"></td>
2858 <td></td>
2859 <td class="paramtype">bool&#160;</td>
2860 <td class="paramname"><em>biasEnabled</em>, </td>
2861 </tr>
2862 <tr>
2863 <td class="paramkey"></td>
2864 <td></td>
2865 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2866 <td class="paramname"><em>layout</em>&#160;</td>
2867 </tr>
2868 <tr>
2869 <td></td>
2870 <td>)</td>
2871 <td></td><td></td>
2872 </tr>
2873 </table>
2874</div><div class="memdoc">
2875
2876</div>
2877</div>
2878<a id="a3097119efa3acd563c309feec628b233"></a>
2879<h2 class="memtitle"><span class="permalink"><a href="#a3097119efa3acd563c309feec628b233">&#9670;&nbsp;</a></span>DepthwiseConvolution2dMult2Test< armnn::DataType::Float32, armnn::DataType::Float32 >()</h2>
2880
2881<div class="memitem">
2882<div class="memproto">
2883 <table class="memname">
2884 <tr>
2885 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#aaed50a372a6b59b20e38469856a3ce6b">DepthwiseConvolution2dMult2Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt; </td>
2886 <td>(</td>
2887 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::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">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2894 <td class="paramname"><em>memoryManager</em>, </td>
2895 </tr>
2896 <tr>
2897 <td class="paramkey"></td>
2898 <td></td>
2899 <td class="paramtype">bool&#160;</td>
2900 <td class="paramname"><em>biasEnabled</em>, </td>
2901 </tr>
2902 <tr>
2903 <td class="paramkey"></td>
2904 <td></td>
2905 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2906 <td class="paramname"><em>layout</em>&#160;</td>
2907 </tr>
2908 <tr>
2909 <td></td>
2910 <td>)</td>
2911 <td></td><td></td>
2912 </tr>
2913 </table>
2914</div><div class="memdoc">
2915
2916</div>
2917</div>
2918<a id="a0da6534b3a5d2f923dcd73553950129a"></a>
2919<h2 class="memtitle"><span class="permalink"><a href="#a0da6534b3a5d2f923dcd73553950129a">&#9670;&nbsp;</a></span>DepthwiseConvolution2dMult4Test()</h2>
2920
2921<div class="memitem">
2922<div class="memproto">
2923 <table class="memname">
2924 <tr>
2925 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dMult4Test </td>
2926 <td>(</td>
2927 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2928 <td class="paramname"><em>workloadFactory</em>, </td>
2929 </tr>
2930 <tr>
2931 <td class="paramkey"></td>
2932 <td></td>
2933 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2934 <td class="paramname"><em>memoryManager</em>, </td>
2935 </tr>
2936 <tr>
2937 <td class="paramkey"></td>
2938 <td></td>
2939 <td class="paramtype">bool&#160;</td>
2940 <td class="paramname"><em>biasEnabled</em>, </td>
2941 </tr>
2942 <tr>
2943 <td class="paramkey"></td>
2944 <td></td>
2945 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2946 <td class="paramname"><em>layout</em>&#160;</td>
2947 </tr>
2948 <tr>
2949 <td></td>
2950 <td>)</td>
2951 <td></td><td></td>
2952 </tr>
2953 </table>
2954</div><div class="memdoc">
2955
2956<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02508">2508</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
2957<div class="fragment"><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>&#160;{</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>&#160; std::vector&lt;float&gt; inputNoQuantizedValues =</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>&#160; {</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>&#160; 10.0, 10.0, 10.0,</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>&#160; 10.0, 10.0, 10.0,</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>&#160; 10.0, 10.0, 10.0,</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>&#160;</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>&#160; 21.0, 22.0, 23.0,</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>&#160; 24.0, 25.0, 26.0,</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>&#160; 27.0, 28.0, 29.0</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>&#160; };</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>&#160;</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 4, 2, 2, 2}, ArmnnType);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>&#160;</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>&#160; std::vector&lt;float&gt; kernelNoQuantizedValues =</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>&#160; {</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>&#160; 0.25f, 0.25f,</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>&#160; 0.25f, 0.25f,</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; 0.25f, 0.25f,</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>&#160; 0.25f, 0.25f,</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>&#160;</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>&#160; 0.0f , 0.0f,</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>&#160; 0.0f , 0.1f,</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>&#160;</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>&#160; 0.0f , 0.0f,</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>&#160; 0.0f , 0.1f,</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; 0.2f , 0.0f,</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>&#160; 0.0f , 0.0f,</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>&#160;</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>&#160; 0.2f , 0.0f,</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>&#160; 0.0f , 0.0f,</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>&#160;</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>&#160; 0.0f , 0.3f,</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>&#160; 0.0f , 0.0f,</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>&#160;</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>&#160; 0.0f , 0.3f,</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>&#160; 0.0f , 0.0f</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>&#160; };</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>&#160;</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 8, 2, 2}, ArmnnType);</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>&#160; std::vector&lt;float&gt; outputExpectedNoQuantizedValues =</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; 10.f, 10.f,</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>&#160; 10.f, 10.f,</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>&#160;</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>&#160;</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>&#160; 2.f, 2.f,</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>&#160; 2.f, 2.f,</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; 3.f, 3.f,</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>&#160; 3.f, 3.f,</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>&#160;</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>&#160; 23.f, 24.f,</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>&#160; 26.f, 27.f,</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>&#160;</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>&#160; 2.5f, 2.6000001f,</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>&#160; 2.8f, 2.9f,</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; 4.2000003f, 4.4f,</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>&#160; 4.8f, 5.f,</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>&#160;</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>&#160; 6.6000004f, 6.9f,</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>&#160; 7.5000005f, 7.8f</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>&#160; };</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>&#160;</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>&#160;</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>&#160; workloadFactory,</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>&#160; memoryManager,</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>&#160; inputNoQuantizedValues,</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>&#160; inputTensorInfo,</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>&#160; kernelNoQuantizedValues,</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>&#160; kernelTensorInfo,</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>&#160; outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>&#160; outputTensorInfo,</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>&#160; 1,</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>&#160; 1,</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>&#160; layout,</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>&#160; biasEnabled);</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</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>
2958</div><!-- fragment -->
2959</div>
2960</div>
2961<a id="a458125d04d00674f4bb30ef5c8d8e74f"></a>
2962<h2 class="memtitle"><span class="permalink"><a href="#a458125d04d00674f4bb30ef5c8d8e74f">&#9670;&nbsp;</a></span>DepthwiseConvolution2dMult4Test< armnn::DataType::BFloat16, armnn::DataType::BFloat16 >()</h2>
2963
2964<div class="memitem">
2965<div class="memproto">
2966 <table class="memname">
2967 <tr>
2968 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a0da6534b3a5d2f923dcd73553950129a">DepthwiseConvolution2dMult4Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a> &gt; </td>
2969 <td>(</td>
2970 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
2971 <td class="paramname"><em>workloadFactory</em>, </td>
2972 </tr>
2973 <tr>
2974 <td class="paramkey"></td>
2975 <td></td>
2976 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
2977 <td class="paramname"><em>memoryManager</em>, </td>
2978 </tr>
2979 <tr>
2980 <td class="paramkey"></td>
2981 <td></td>
2982 <td class="paramtype">bool&#160;</td>
2983 <td class="paramname"><em>biasEnabled</em>, </td>
2984 </tr>
2985 <tr>
2986 <td class="paramkey"></td>
2987 <td></td>
2988 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
2989 <td class="paramname"><em>layout</em>&#160;</td>
2990 </tr>
2991 <tr>
2992 <td></td>
2993 <td>)</td>
2994 <td></td><td></td>
2995 </tr>
2996 </table>
2997</div><div class="memdoc">
2998
2999</div>
3000</div>
3001<a id="a52590a78e77f52f9be313967c35b870b"></a>
3002<h2 class="memtitle"><span class="permalink"><a href="#a52590a78e77f52f9be313967c35b870b">&#9670;&nbsp;</a></span>DepthwiseConvolution2dMult4Test< armnn::DataType::Float32, armnn::DataType::Float32 >()</h2>
3003
3004<div class="memitem">
3005<div class="memproto">
3006 <table class="memname">
3007 <tr>
3008 <td class="memname">template <a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;<a class="el" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a>&lt;<a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>&gt;, 4&gt; <a class="el" href="_conv2d_test_impl_8hpp.xhtml#a0da6534b3a5d2f923dcd73553950129a">DepthwiseConvolution2dMult4Test</a>&lt; <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a> &gt; </td>
3009 <td>(</td>
3010 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3011 <td class="paramname"><em>workloadFactory</em>, </td>
3012 </tr>
3013 <tr>
3014 <td class="paramkey"></td>
3015 <td></td>
3016 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3017 <td class="paramname"><em>memoryManager</em>, </td>
3018 </tr>
3019 <tr>
3020 <td class="paramkey"></td>
3021 <td></td>
3022 <td class="paramtype">bool&#160;</td>
3023 <td class="paramname"><em>biasEnabled</em>, </td>
3024 </tr>
3025 <tr>
3026 <td class="paramkey"></td>
3027 <td></td>
3028 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3029 <td class="paramname"><em>layout</em>&#160;</td>
3030 </tr>
3031 <tr>
3032 <td></td>
3033 <td>)</td>
3034 <td></td><td></td>
3035 </tr>
3036 </table>
3037</div><div class="memdoc">
3038
3039</div>
3040</div>
3041<a id="a6271caa80dbf6fc82f97081d3d99d987"></a>
3042<h2 class="memtitle"><span class="permalink"><a href="#a6271caa80dbf6fc82f97081d3d99d987">&#9670;&nbsp;</a></span>DepthwiseConvolution2dNhwcTestCommon()</h2>
3043
3044<div class="memitem">
3045<div class="memproto">
3046 <table class="memname">
3047 <tr>
3048 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dNhwcTestCommon </td>
3049 <td>(</td>
3050 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3051 <td class="paramname"><em>workloadFactory</em>, </td>
3052 </tr>
3053 <tr>
3054 <td class="paramkey"></td>
3055 <td></td>
3056 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3057 <td class="paramname"><em>memoryManager</em>, </td>
3058 </tr>
3059 <tr>
3060 <td class="paramkey"></td>
3061 <td></td>
3062 <td class="paramtype">float&#160;</td>
3063 <td class="paramname"><em>qScale</em>, </td>
3064 </tr>
3065 <tr>
3066 <td class="paramkey"></td>
3067 <td></td>
3068 <td class="paramtype">int32_t&#160;</td>
3069 <td class="paramname"><em>qOffset</em>, </td>
3070 </tr>
3071 <tr>
3072 <td class="paramkey"></td>
3073 <td></td>
3074 <td class="paramtype">bool&#160;</td>
3075 <td class="paramname"><em>biasEnabled</em>&#160;</td>
3076 </tr>
3077 <tr>
3078 <td></td>
3079 <td>)</td>
3080 <td></td><td></td>
3081 </tr>
3082 </table>
3083</div><div class="memdoc">
3084
3085<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02131">2131</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3086
3087<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p>
3088<div class="fragment"><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; <span class="keyword">auto</span> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 2, 5, 5}, ArmnnType);</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>&#160; 0, 1, 2, 3, 4,</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>&#160; 5, 6, 7, 8, 9,</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>&#160; 10, 11, 12, 13, 14,</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>&#160; 15, 16, 17, 18, 19,</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>&#160; 20, 21, 22, 23, 24,</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; 25, 26, 27, 28, 29,</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>&#160; 30, 31, 32, 33, 34,</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>&#160; 35, 36, 37, 38, 39,</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>&#160; 40, 41, 42, 43, 44,</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>&#160; 45, 46, 47, 48, 49</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>&#160; },</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>&#160; inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>&#160; inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>&#160;</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType);</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>&#160; <span class="keyword">auto</span> kernel = MakeTensor&lt;T, 4&gt;(kernelTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>&#160; 32, 31, 30, 29,</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>&#160; 28, 27, 26, 25,</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>&#160; 24, 23, 22, 21,</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>&#160; 20, 19, 18, 17,</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; 16, 15, 14, 13,</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>&#160; 12, 11, 10, 9,</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>&#160; 8, 7, 6, 5,</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>&#160; 4, 3, 2, 1</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; kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>&#160; kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>&#160;</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2, 5, 5}, ArmnnType);</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>&#160; 1062, 1580, 1850, 1530, 1117,</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>&#160; 2140, 3108, 3500, 2842, 2042,</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>&#160; 3580, 5068, 5460, 4342, 3062,</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>&#160; 3618, 5072, 5390, 4248, 2971,</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>&#160; 3074, 4282, 4510, 3533, 2457,</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>&#160;</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>&#160; 1550, 2284, 2362, 1955, 1428,</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>&#160; 2910, 4206, 4342, 3528, 2536,</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>&#160; 3390, 4886, 5022, 4068, 2916,</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>&#160; 3566, 5056, 5182, 4133, 2922,</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>&#160; 3100, 4352, 4452, 3517, 2465</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>&#160; },</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>&#160; outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>&#160; outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>&#160;</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>&#160; workloadFactory,</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>&#160; memoryManager,</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>&#160; input,</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>&#160; kernel,</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>&#160; GetBias2&lt;ArmnnBType&gt;(biasEnabled, qScale * qScale),</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>&#160; expectedOutput,</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>&#160; qScale,</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>&#160; qOffset,</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>&#160; layout,</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>&#160; 1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>&#160; 1, <span class="comment">// Padding top.</span></div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>&#160; 2, <span class="comment">// Padding right.</span></div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>&#160; 2, <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>&#160; 1, <span class="comment">// strideX</span></div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>&#160; 1); <span class="comment">// strideY</span></div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</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>
3089<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
3090</div><!-- fragment -->
3091</div>
3092</div>
3093<a id="a8a51827c480f827c1e29f9347d7433c3"></a>
3094<h2 class="memtitle"><span class="permalink"><a href="#a8a51827c480f827c1e29f9347d7433c3">&#9670;&nbsp;</a></span>DepthwiseConvolution2dPerAxisQuantTest()</h2>
3095
3096<div class="memitem">
3097<div class="memproto">
3098 <table class="memname">
3099 <tr>
3100 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; DepthwiseConvolution2dPerAxisQuantTest </td>
3101 <td>(</td>
3102 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3103 <td class="paramname"><em>workloadFactory</em>, </td>
3104 </tr>
3105 <tr>
3106 <td class="paramkey"></td>
3107 <td></td>
3108 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3109 <td class="paramname"><em>memoryManager</em>, </td>
3110 </tr>
3111 <tr>
3112 <td class="paramkey"></td>
3113 <td></td>
3114 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3115 <td class="paramname"><em>layout</em>&#160;</td>
3116 </tr>
3117 <tr>
3118 <td></td>
3119 <td>)</td>
3120 <td></td><td></td>
3121 </tr>
3122 </table>
3123</div><div class="memdoc">
3124
3125<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03316">3316</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3126
3127<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <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#l01177">IWorkloadFactory::CreateDepthwiseConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00490">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00492">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00486">DepthwiseConvolution2dDescriptor::m_DilationX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00488">DepthwiseConvolution2dDescriptor::m_DilationY</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00480">DepthwiseConvolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00474">DepthwiseConvolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00476">DepthwiseConvolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00478">DepthwiseConvolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00482">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00484">DepthwiseConvolution2dDescriptor::m_StrideY</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</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="_data_layout_utils_8hpp_source.xhtml#l00026">PermuteTensorNhwcToNchw()</a>.</p>
3128<div class="fragment"><div class="line"><a name="l03320"></a><span class="lineno"> 3320</span>&#160;{</div><div class="line"><a name="l03321"></a><span class="lineno"> 3321</span>&#160; <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l03322"></a><span class="lineno"> 3322</span>&#160;</div><div class="line"><a name="l03323"></a><span class="lineno"> 3323</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>;</div><div class="line"><a name="l03324"></a><span class="lineno"> 3324</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> kernelType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>;</div><div class="line"><a name="l03325"></a><span class="lineno"> 3325</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> biasType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>;</div><div class="line"><a name="l03326"></a><span class="lineno"> 3326</span>&#160;</div><div class="line"><a name="l03327"></a><span class="lineno"> 3327</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo ({ 1, 3, 3, 2 }, inputType, 0.5f, 128); <span class="comment">// N H W C</span></div><div class="line"><a name="l03328"></a><span class="lineno"> 3328</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ 1, 2, 2, 4 }, inputType, 1.0f, 128); <span class="comment">// N H W C</span></div><div class="line"><a name="l03329"></a><span class="lineno"> 3329</span>&#160;</div><div class="line"><a name="l03330"></a><span class="lineno"> 3330</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; quantScales{ 1.0f, 0.5f, 1.0f, 0.5f };</div><div class="line"><a name="l03331"></a><span class="lineno"> 3331</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> quantDimension = 0;</div><div class="line"><a name="l03332"></a><span class="lineno"> 3332</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> kernelInfo({ 2, 2, 2, 2 }, kernelType, quantScales, quantDimension); <span class="comment">// M I H W</span></div><div class="line"><a name="l03333"></a><span class="lineno"> 3333</span>&#160;</div><div class="line"><a name="l03334"></a><span class="lineno"> 3334</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; biasQuantScales{ 0.5f, 0.25f, 0.5f, 0.25f };</div><div class="line"><a name="l03335"></a><span class="lineno"> 3335</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasQuantDimension = 0;</div><div class="line"><a name="l03336"></a><span class="lineno"> 3336</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({ 4 }, biasType, biasQuantScales, biasQuantDimension);</div><div class="line"><a name="l03337"></a><span class="lineno"> 3337</span>&#160;</div><div class="line"><a name="l03338"></a><span class="lineno"> 3338</span>&#160; std::vector&lt;uint8_t&gt; inputData =</div><div class="line"><a name="l03339"></a><span class="lineno"> 3339</span>&#160; {</div><div class="line"><a name="l03340"></a><span class="lineno"> 3340</span>&#160; 129, 130,</div><div class="line"><a name="l03341"></a><span class="lineno"> 3341</span>&#160; 129, 130,</div><div class="line"><a name="l03342"></a><span class="lineno"> 3342</span>&#160; 129, 130,</div><div class="line"><a name="l03343"></a><span class="lineno"> 3343</span>&#160; 129, 130,</div><div class="line"><a name="l03344"></a><span class="lineno"> 3344</span>&#160; 129, 130,</div><div class="line"><a name="l03345"></a><span class="lineno"> 3345</span>&#160; 129, 130,</div><div class="line"><a name="l03346"></a><span class="lineno"> 3346</span>&#160; 129, 130,</div><div class="line"><a name="l03347"></a><span class="lineno"> 3347</span>&#160; 129, 130,</div><div class="line"><a name="l03348"></a><span class="lineno"> 3348</span>&#160; 129, 130</div><div class="line"><a name="l03349"></a><span class="lineno"> 3349</span>&#160; };</div><div class="line"><a name="l03350"></a><span class="lineno"> 3350</span>&#160;</div><div class="line"><a name="l03351"></a><span class="lineno"> 3351</span>&#160; std::vector&lt;int8_t&gt; kernelData =</div><div class="line"><a name="l03352"></a><span class="lineno"> 3352</span>&#160; {</div><div class="line"><a name="l03353"></a><span class="lineno"> 3353</span>&#160; 1, 1, 1, 1,</div><div class="line"><a name="l03354"></a><span class="lineno"> 3354</span>&#160; 1, 1, 1, 1,</div><div class="line"><a name="l03355"></a><span class="lineno"> 3355</span>&#160; 1, 1, 1, 1,</div><div class="line"><a name="l03356"></a><span class="lineno"> 3356</span>&#160; 1, 1, 1, 1</div><div class="line"><a name="l03357"></a><span class="lineno"> 3357</span>&#160; };</div><div class="line"><a name="l03358"></a><span class="lineno"> 3358</span>&#160;</div><div class="line"><a name="l03359"></a><span class="lineno"> 3359</span>&#160; std::vector&lt;int32_t&gt; biasData =</div><div class="line"><a name="l03360"></a><span class="lineno"> 3360</span>&#160; {</div><div class="line"><a name="l03361"></a><span class="lineno"> 3361</span>&#160; 4, 4, 4, 4</div><div class="line"><a name="l03362"></a><span class="lineno"> 3362</span>&#160; };</div><div class="line"><a name="l03363"></a><span class="lineno"> 3363</span>&#160;</div><div class="line"><a name="l03364"></a><span class="lineno"> 3364</span>&#160; std::vector&lt;uint8_t&gt; expectedOutputData =</div><div class="line"><a name="l03365"></a><span class="lineno"> 3365</span>&#160; {</div><div class="line"><a name="l03366"></a><span class="lineno"> 3366</span>&#160; 132, 130, 134, 131,</div><div class="line"><a name="l03367"></a><span class="lineno"> 3367</span>&#160; 132, 130, 134, 131,</div><div class="line"><a name="l03368"></a><span class="lineno"> 3368</span>&#160; 132, 130, 134, 131,</div><div class="line"><a name="l03369"></a><span class="lineno"> 3369</span>&#160; 132, 130, 134, 131</div><div class="line"><a name="l03370"></a><span class="lineno"> 3370</span>&#160; };</div><div class="line"><a name="l03371"></a><span class="lineno"> 3371</span>&#160;</div><div class="line"><a name="l03372"></a><span class="lineno"> 3372</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l03373"></a><span class="lineno"> 3373</span>&#160; {</div><div class="line"><a name="l03374"></a><span class="lineno"> 3374</span>&#160; <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(inputInfo, inputData);</div><div class="line"><a name="l03375"></a><span class="lineno"> 3375</span>&#160; <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(outputInfo, expectedOutputData);</div><div class="line"><a name="l03376"></a><span class="lineno"> 3376</span>&#160; }</div><div class="line"><a name="l03377"></a><span class="lineno"> 3377</span>&#160;</div><div class="line"><a name="l03378"></a><span class="lineno"> 3378</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l03379"></a><span class="lineno"> 3379</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l03380"></a><span class="lineno"> 3380</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l03381"></a><span class="lineno"> 3381</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l03382"></a><span class="lineno"> 3382</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l03383"></a><span class="lineno"> 3383</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l03384"></a><span class="lineno"> 3384</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l03385"></a><span class="lineno"> 3385</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 1;</div><div class="line"><a name="l03386"></a><span class="lineno"> 3386</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 1;</div><div class="line"><a name="l03387"></a><span class="lineno"> 3387</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03388"></a><span class="lineno"> 3388</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l03389"></a><span class="lineno"> 3389</span>&#160;</div><div class="line"><a name="l03390"></a><span class="lineno"> 3390</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputInfo);</div><div class="line"><a name="l03391"></a><span class="lineno"> 3391</span>&#160; std::unique_ptr&lt;ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputInfo);</div><div class="line"><a name="l03392"></a><span class="lineno"> 3392</span>&#160;</div><div class="line"><a name="l03393"></a><span class="lineno"> 3393</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l03394"></a><span class="lineno"> 3394</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(kernelInfo);</div><div class="line"><a name="l03395"></a><span class="lineno"> 3395</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasTensor(biasInfo);</div><div class="line"><a name="l03396"></a><span class="lineno"> 3396</span>&#160;</div><div class="line"><a name="l03397"></a><span class="lineno"> 3397</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightTensor, kernelData.data());</div><div class="line"><a name="l03398"></a><span class="lineno"> 3398</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, biasData.data());</div><div class="line"><a name="l03399"></a><span class="lineno"> 3399</span>&#160;</div><div class="line"><a name="l03400"></a><span class="lineno"> 3400</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">DepthwiseConvolution2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l03401"></a><span class="lineno"> 3401</span>&#160; queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = descriptor;</div><div class="line"><a name="l03402"></a><span class="lineno"> 3402</span>&#160; queueDescriptor.m_Weight = &amp;weightTensor;</div><div class="line"><a name="l03403"></a><span class="lineno"> 3403</span>&#160; queueDescriptor.m_Bias = &amp;biasTensor;</div><div class="line"><a name="l03404"></a><span class="lineno"> 3404</span>&#160;</div><div class="line"><a name="l03405"></a><span class="lineno"> 3405</span>&#160; AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());</div><div class="line"><a name="l03406"></a><span class="lineno"> 3406</span>&#160; AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());</div><div class="line"><a name="l03407"></a><span class="lineno"> 3407</span>&#160;</div><div class="line"><a name="l03408"></a><span class="lineno"> 3408</span>&#160; std::unique_ptr&lt;IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(queueDescriptor, workloadInfo);</div><div class="line"><a name="l03409"></a><span class="lineno"> 3409</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l03410"></a><span class="lineno"> 3410</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l03411"></a><span class="lineno"> 3411</span>&#160;</div><div class="line"><a name="l03412"></a><span class="lineno"> 3412</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData.data());</div><div class="line"><a name="l03413"></a><span class="lineno"> 3413</span>&#160;</div><div class="line"><a name="l03414"></a><span class="lineno"> 3414</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l03415"></a><span class="lineno"> 3415</span>&#160;</div><div class="line"><a name="l03416"></a><span class="lineno"> 3416</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> ret(outputInfo);</div><div class="line"><a name="l03417"></a><span class="lineno"> 3417</span>&#160;</div><div class="line"><a name="l03418"></a><span class="lineno"> 3418</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(ret.output.origin(), outputHandle.get());</div><div class="line"><a name="l03419"></a><span class="lineno"> 3419</span>&#160; ret.outputExpected = MakeTensor&lt;uint8_t, 4&gt;(outputInfo, expectedOutputData);</div><div class="line"><a name="l03420"></a><span class="lineno"> 3420</span>&#160;</div><div class="line"><a name="l03421"></a><span class="lineno"> 3421</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l03422"></a><span class="lineno"> 3422</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div>
3129<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div>
3130<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div>
3131<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>
3132<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
3133<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div>
3134<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div>
3135<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>
3136<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::DepthwiseConvolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation factor value for height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00488">Descriptors.hpp:488</a></div></div>
3137<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</a></div></div>
3138<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
3139<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::DepthwiseConvolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation factor value for width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00486">Descriptors.hpp:486</a></div></div>
3140<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div>
3141<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
3142<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
3143<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>
3144<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>
3145<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
3146<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div>
3147<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>
3148<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>
3149<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
3150<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
3151<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_accb9759dfd2880efe0f8d2705ddee448"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">armnn::IWorkloadFactory::CreateDepthwiseConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01177">WorkloadFactory.cpp:1177</a></div></div>
3152<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00444">Descriptors.hpp:444</a></div></div>
3153<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00183">WorkloadData.hpp:183</a></div></div>
3154<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>
3155<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div>
3156<div class="ttc" id="_data_layout_utils_8hpp_xhtml_a1452f049aef30409b3b649af2be7ff82"><div class="ttname"><a href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a></div><div class="ttdeci">void PermuteTensorNhwcToNchw(armnn::TensorInfo &amp;tensorInfo, std::vector&lt; T &gt; &amp;tensorData)</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_utils_8hpp_source.xhtml#l00026">DataLayoutUtils.hpp:26</a></div></div>
3157</div><!-- fragment -->
3158</div>
3159</div>
3160<a id="a11fbd94028ab646528b42d0c8c55eee1"></a>
3161<h2 class="memtitle"><span class="permalink"><a href="#a11fbd94028ab646528b42d0c8c55eee1">&#9670;&nbsp;</a></span>DepthwiseConvolution2dTest()</h2>
3162
3163<div class="memitem">
3164<div class="memproto">
3165 <table class="memname">
3166 <tr>
3167 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; DepthwiseConvolution2dTest </td>
3168 <td>(</td>
3169 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3170 <td class="paramname"><em>workloadFactory</em>, </td>
3171 </tr>
3172 <tr>
3173 <td class="paramkey"></td>
3174 <td></td>
3175 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3176 <td class="paramname"><em>memoryManager</em>, </td>
3177 </tr>
3178 <tr>
3179 <td class="paramkey"></td>
3180 <td></td>
3181 <td class="paramtype">bool&#160;</td>
3182 <td class="paramname"><em>biasEnabled</em>, </td>
3183 </tr>
3184 <tr>
3185 <td class="paramkey"></td>
3186 <td></td>
3187 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3188 <td class="paramname"><em>layout</em>&#160;</td>
3189 </tr>
3190 <tr>
3191 <td></td>
3192 <td>)</td>
3193 <td></td><td></td>
3194 </tr>
3195 </table>
3196</div><div class="memdoc">
3197
3198<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03193">3193</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3199<div class="fragment"><div class="line"><a name="l03198"></a><span class="lineno"> 3198</span>&#160;{</div><div class="line"><a name="l03199"></a><span class="lineno"> 3199</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03200"></a><span class="lineno"> 3200</span>&#160; workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);</div><div class="line"><a name="l03201"></a><span class="lineno"> 3201</span>&#160;}</div></div><!-- fragment -->
3200</div>
3201</div>
3202<a id="ae3cc54b77789d10caeb5a438a0821ba0"></a>
3203<h2 class="memtitle"><span class="permalink"><a href="#ae3cc54b77789d10caeb5a438a0821ba0">&#9670;&nbsp;</a></span>DepthwiseConvolution2dTestImpl() <span class="overload">[1/2]</span></h2>
3204
3205<div class="memitem">
3206<div class="memproto">
3207 <table class="memname">
3208 <tr>
3209 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dTestImpl </td>
3210 <td>(</td>
3211 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3212 <td class="paramname"><em>workloadFactory</em>, </td>
3213 </tr>
3214 <tr>
3215 <td class="paramkey"></td>
3216 <td></td>
3217 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3218 <td class="paramname"><em>memoryManager</em>, </td>
3219 </tr>
3220 <tr>
3221 <td class="paramkey"></td>
3222 <td></td>
3223 <td class="paramtype">float&#160;</td>
3224 <td class="paramname"><em>qScale</em>, </td>
3225 </tr>
3226 <tr>
3227 <td class="paramkey"></td>
3228 <td></td>
3229 <td class="paramtype">int32_t&#160;</td>
3230 <td class="paramname"><em>qOffset</em>, </td>
3231 </tr>
3232 <tr>
3233 <td class="paramkey"></td>
3234 <td></td>
3235 <td class="paramtype">bool&#160;</td>
3236 <td class="paramname"><em>biasEnabled</em>, </td>
3237 </tr>
3238 <tr>
3239 <td class="paramkey"></td>
3240 <td></td>
3241 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3242 <td class="paramname"><em>layout</em>&#160;</td>
3243 </tr>
3244 <tr>
3245 <td></td>
3246 <td>)</td>
3247 <td></td><td></td>
3248 </tr>
3249 </table>
3250</div><div class="memdoc">
3251
3252<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01671">1671</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3253
3254<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00169">ApplyBias()</a>, <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#l01177">IWorkloadFactory::CreateDepthwiseConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00264">TensorInfo::GetQuantizationOffset()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00247">TensorInfo::GetQuantizationScale()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_tensor_utils_8cpp_source.xhtml#l00038">armnnUtils::GetTensorInfo()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00192">DepthwiseConvolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00490">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00492">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00480">DepthwiseConvolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00474">DepthwiseConvolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00476">DepthwiseConvolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00478">DepthwiseConvolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00482">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00484">DepthwiseConvolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00191">DepthwiseConvolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, <a class="el" href="_permute_8cpp_source.xhtml#l00121">armnnUtils::Permute()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00275">TensorInfo::SetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
3255<div class="fragment"><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>&#160;{</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>&#160; <span class="keyword">using</span> <a class="code" href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">B</a> = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType&lt;ArmnnBType&gt;</a>;</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; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier = 2;</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>&#160;</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 8;</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 2;</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</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; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 5;</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>&#160;</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight - kernelHeight + 1 + 2;</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth - kernelWidth + 1)/2;</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels * depthMultiplier;</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>&#160;</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>&#160; inputBatchSize, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>&#160; outputBatchSize, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth},</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>&#160; ArmnnType);</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({outputChannels}, ArmnnBType);</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="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>&#160; <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>&#160; {</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>&#160; kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>&#160; kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>&#160; biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>&#160; biasDesc.SetQuantizationOffset(0);</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;</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>&#160; <span class="comment">// NOTE: originalInputData is in NCHW format</span></div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>&#160; std::vector&lt;T&gt; originalInputData = std::vector&lt;T&gt;(</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>&#160; 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>&#160; 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>&#160; 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>&#160; 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>&#160; 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>&#160; 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>&#160; 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>&#160; 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f</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; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>&#160;</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>&#160; std::vector&lt;T&gt; inputData = originalInputData;</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>&#160; <span class="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>&#160; {</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC,</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>&#160; originalInputData.data(), inputData.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>&#160; }</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>&#160;</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>&#160; std::vector&lt;B&gt; biasV = QuantizedVector&lt;B&gt;({ 0, 2, 1, -1 },</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>&#160; biasDesc.GetQuantizationScale(),</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>&#160; biasDesc.GetQuantizationOffset());</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; <span class="keyword">auto</span> bias = MakeTensor&lt;B, 1&gt;(biasDesc, biasV);</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>&#160;</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>&#160; std::vector&lt;T&gt; kernelData = std::vector&lt;T&gt;(</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>&#160; 1, 1, 1,</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>&#160; 1, -1, 1,</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>&#160; 1, 1, 1,</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>&#160; 1, 1, 1,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>&#160; 1, 1, 1,</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>&#160;</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>&#160; 2, 2, 2,</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>&#160; 2, 2, 2,</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>&#160; 2, 2, 2,</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>&#160; 2, 2, 2,</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>&#160; 2, 2, 2,</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; 0, 0, 0,</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>&#160; 0, -1, 0,</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>&#160; 0, 0, 0,</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>&#160; 0, 0, 0,</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>&#160; 0, 0, 0,</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>&#160;</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>&#160; 0, 0, 0,</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>&#160; 0, 0, 0,</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>&#160; 0, 1, 0,</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>&#160; 0, 0, 0,</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>&#160; 0, 0, 0</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; kernelDesc.GetQuantizationScale(),</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>&#160; kernelDesc.GetQuantizationOffset()));</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; <span class="keyword">auto</span> kernel = MakeTensor&lt;T, 4&gt;(kernelDesc, kernelData);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>&#160;</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>&#160; <span class="comment">// Manually calculated.</span></div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>&#160; std::vector&lt;T&gt; originalOutputImage = std::vector&lt;T&gt;(</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>&#160; 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f,</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>&#160; 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f,</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>&#160; 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f,</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>&#160; 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f,</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>&#160; 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f,</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>&#160; 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f,</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; -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>&#160; -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>&#160; -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>&#160; -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>&#160; -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>&#160;</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>&#160; 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>&#160; 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>&#160; 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>&#160; 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>&#160; 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>&#160; 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>&#160;</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>&#160; },</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>&#160;</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>&#160; <span class="comment">// Optionally apply bias to output image.</span></div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>&#160; {</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>&#160; <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(originalOutputImage,</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>&#160; biasV,</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>&#160; biasDesc.GetQuantizationScale(),</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>&#160; biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>&#160; outputWidth,</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>&#160; outputHeight);</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>&#160; }</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>&#160;</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>&#160; std::vector&lt;T&gt; outputImage = originalOutputImage;</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC,</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>&#160; originalOutputImage.data(), outputImage.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>&#160; }</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>&#160;</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>&#160; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, outputImage);</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; std::unique_ptr&lt;armnn::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="l01845"></a><span class="lineno"> 1845</span>&#160; std::unique_ptr&lt;armnn::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="l01846"></a><span class="lineno"> 1846</span>&#160;</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>&#160;</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, &amp;bias[0]);</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>&#160;</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>&#160;</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor; <span class="comment">// Still set this whether or not bias is enabled.</span></div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</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; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>&#160;</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>&#160;</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>&#160; ExecuteWorkload(*workload, memoryManager);</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; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>&#160;</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div>
3256<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>
3257<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div>
3258<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa1f4ce02e0904dc8cf1b7f42bc34d346"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a></div><div class="ttdeci">void ApplyBias(std::vector&lt; T &gt; &amp;v, float vScale, int32_t vOffset, const std::vector&lt; B &gt; &amp;bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00169">Conv2dTestImpl.cpp:169</a></div></div>
3259<div class="ttc" id="_inference_test_image_8hpp_xhtml_a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571"><div class="ttname"><a href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">ImageChannel::B</a></div></div>
3260<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div>
3261<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>
3262<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::DepthwiseConvolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00192">WorkloadData.hpp:192</a></div></div>
3263<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div>
3264<div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl&lt; DT &gt;::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00073">ResolveType.hpp:73</a></div></div>
3265<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>
3266<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::DepthwiseConvolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00191">WorkloadData.hpp:191</a></div></div>
3267<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div>
3268<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</a></div></div>
3269<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div>
3270<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div>
3271<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</a></div></div>
3272<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>
3273<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
3274<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>
3275<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>
3276<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>
3277<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
3278<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div>
3279<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
3280<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div>
3281<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>
3282<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>
3283<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div>
3284<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_accb9759dfd2880efe0f8d2705ddee448"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">armnn::IWorkloadFactory::CreateDepthwiseConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01177">WorkloadFactory.cpp:1177</a></div></div>
3285<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00183">WorkloadData.hpp:183</a></div></div>
3286<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
3287<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>
3288<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div>
3289</div><!-- fragment -->
3290</div>
3291</div>
3292<a id="a46e9706106f1b08c964d953154c66ad6"></a>
3293<h2 class="memtitle"><span class="permalink"><a href="#a46e9706106f1b08c964d953154c66ad6">&#9670;&nbsp;</a></span>DepthwiseConvolution2dTestImpl() <span class="overload">[2/2]</span></h2>
3294
3295<div class="memitem">
3296<div class="memproto">
3297 <table class="memname">
3298 <tr>
3299 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; DepthwiseConvolution2dTestImpl </td>
3300 <td>(</td>
3301 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3302 <td class="paramname"><em>workloadFactory</em>, </td>
3303 </tr>
3304 <tr>
3305 <td class="paramkey"></td>
3306 <td></td>
3307 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3308 <td class="paramname"><em>memoryManager</em>, </td>
3309 </tr>
3310 <tr>
3311 <td class="paramkey"></td>
3312 <td></td>
3313 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
3314 <td class="paramname"><em>originalInput</em>, </td>
3315 </tr>
3316 <tr>
3317 <td class="paramkey"></td>
3318 <td></td>
3319 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
3320 <td class="paramname"><em>originalKernel</em>, </td>
3321 </tr>
3322 <tr>
3323 <td class="paramkey"></td>
3324 <td></td>
3325 <td class="paramtype">const boost::multi_array&lt; B, 1 &gt; &amp;&#160;</td>
3326 <td class="paramname"><em>bias</em>, </td>
3327 </tr>
3328 <tr>
3329 <td class="paramkey"></td>
3330 <td></td>
3331 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
3332 <td class="paramname"><em>originalOutputExpected</em>, </td>
3333 </tr>
3334 <tr>
3335 <td class="paramkey"></td>
3336 <td></td>
3337 <td class="paramtype">float&#160;</td>
3338 <td class="paramname"><em>qScale</em>, </td>
3339 </tr>
3340 <tr>
3341 <td class="paramkey"></td>
3342 <td></td>
3343 <td class="paramtype">int32_t&#160;</td>
3344 <td class="paramname"><em>qOffset</em>, </td>
3345 </tr>
3346 <tr>
3347 <td class="paramkey"></td>
3348 <td></td>
3349 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3350 <td class="paramname"><em>layout</em> = <code><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></code>, </td>
3351 </tr>
3352 <tr>
3353 <td class="paramkey"></td>
3354 <td></td>
3355 <td class="paramtype">uint32_t&#160;</td>
3356 <td class="paramname"><em>padLeft</em> = <code>0</code>, </td>
3357 </tr>
3358 <tr>
3359 <td class="paramkey"></td>
3360 <td></td>
3361 <td class="paramtype">uint32_t&#160;</td>
3362 <td class="paramname"><em>padTop</em> = <code>0</code>, </td>
3363 </tr>
3364 <tr>
3365 <td class="paramkey"></td>
3366 <td></td>
3367 <td class="paramtype">uint32_t&#160;</td>
3368 <td class="paramname"><em>padRight</em> = <code>0</code>, </td>
3369 </tr>
3370 <tr>
3371 <td class="paramkey"></td>
3372 <td></td>
3373 <td class="paramtype">uint32_t&#160;</td>
3374 <td class="paramname"><em>padBottom</em> = <code>0</code>, </td>
3375 </tr>
3376 <tr>
3377 <td class="paramkey"></td>
3378 <td></td>
3379 <td class="paramtype">uint32_t&#160;</td>
3380 <td class="paramname"><em>strideX</em> = <code>1</code>, </td>
3381 </tr>
3382 <tr>
3383 <td class="paramkey"></td>
3384 <td></td>
3385 <td class="paramtype">uint32_t&#160;</td>
3386 <td class="paramname"><em>strideY</em> = <code>1</code>, </td>
3387 </tr>
3388 <tr>
3389 <td class="paramkey"></td>
3390 <td></td>
3391 <td class="paramtype">uint32_t&#160;</td>
3392 <td class="paramname"><em>dilationX</em> = <code>1</code>, </td>
3393 </tr>
3394 <tr>
3395 <td class="paramkey"></td>
3396 <td></td>
3397 <td class="paramtype">uint32_t&#160;</td>
3398 <td class="paramname"><em>dilationY</em> = <code>1</code>&#160;</td>
3399 </tr>
3400 <tr>
3401 <td></td>
3402 <td>)</td>
3403 <td></td><td></td>
3404 </tr>
3405 </table>
3406</div><div class="memdoc">
3407
3408<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l01884">1884</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3409
3410<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00169">ApplyBias()</a>, <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#l01177">IWorkloadFactory::CreateDepthwiseConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00264">TensorInfo::GetQuantizationOffset()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00247">TensorInfo::GetQuantizationScale()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_tensor_utils_8cpp_source.xhtml#l00038">armnnUtils::GetTensorInfo()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00192">DepthwiseConvolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00490">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00492">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00486">DepthwiseConvolution2dDescriptor::m_DilationX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00488">DepthwiseConvolution2dDescriptor::m_DilationY</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00480">DepthwiseConvolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00474">DepthwiseConvolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00476">DepthwiseConvolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00478">DepthwiseConvolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00482">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00484">DepthwiseConvolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00191">DepthwiseConvolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, <a class="el" href="_numeric_cast_8hpp_source.xhtml#l00033">armnn::numeric_cast()</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="_permute_8cpp_source.xhtml#l00121">armnnUtils::Permute()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00275">TensorInfo::SetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
3411<div class="fragment"><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; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[2]);</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[3]);</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[1]);</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[0]);</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>&#160;</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[2]);</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[3]);</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[1]);</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[0]);</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>&#160;</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[2]);</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[3]);</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[1]);</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelDepthMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[0]);</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>&#160;</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>&#160; <span class="keywordtype">bool</span> biasEnabled = bias.size() &gt; 0;</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>&#160;</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160; <span class="comment">// This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).</span></div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160; BOOST_ASSERT(inputNum == 1);</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160; BOOST_ASSERT(outputNum == 1);</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>&#160;</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160; <span class="comment">// If a bias is used, its size must equal the number of output channels.</span></div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160; BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160;</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; <span class="comment">// Note these tensors will use two (identical) batches.</span></div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</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="comment">// Kernel must be NCHW layout always, independently of the layout of the input and output for depthwise convolution.</span></div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160;</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(bias.size())}, ArmnnBType);</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160;</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160; <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160; <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160; {</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160; inputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160; inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160; kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160; kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160; biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160; biasDesc.SetQuantizationOffset(0);</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;</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160; <span class="comment">// Construct input data</span></div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160; std::vector&lt;T&gt; input;</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160; input.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160; std::vector&lt;T&gt; inputData;</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160; inputData.insert(inputData.end(), input.begin(), input.end());</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160; inputData.insert(inputData.end(), input.begin(), input.end());</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160;</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160; <span class="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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; std::vector&lt;T&gt; tmp(inputData.size());</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160; inputData = tmp;</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;</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160; <span class="keyword">auto</span> batchedInput = MakeTensor&lt;T, 4&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160; std::vector&lt;T&gt; output;</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160; output.assign(originalOutputExpected.data(),</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160; originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160;</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160; <span class="comment">// Apply bias to output data if it is enabled.</span></div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160; {</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160; std::vector&lt;T&gt; biasV;</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160; biasV.assign(bias.data(), bias.data() + outputChannels);</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160; <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(output, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160; biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160; outputWidth, outputHeight);</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160; }</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160;</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160; <span class="comment">// Construct expected output data</span></div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160; std::vector&lt;T&gt; outputData;</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160; outputData.insert(outputData.end(), output.begin(), output.end());</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160; outputData.insert(outputData.end(), output.begin(), output.end());</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160;</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160; <span class="comment">// at this point if we require it permute the expected output</span></div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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; std::vector&lt;T&gt; tmp(outputData.size());</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160; outputData = tmp;</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160; }</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, outputData);</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160; std::unique_ptr&lt;armnn::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="l02001"></a><span class="lineno"> 2001</span>&#160; std::unique_ptr&lt;armnn::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="l02002"></a><span class="lineno"> 2002</span>&#160;</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160; <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</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; boost::multi_array&lt;T, 4&gt; kernel = boost::multi_array&lt;T, 4&gt;(originalKernel);</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160; {</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, &amp;bias[0]);</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160; }</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160; data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor; <span class="comment">// Still set this whether or not bias is enabled - can be a source of bugs.</span></div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</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>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = dilationX;</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = dilationY;</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; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160;</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;batchedInput[0][0][0][0]);</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160;</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160;</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>&#160;</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div>
3412<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>
3413<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div>
3414<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa1f4ce02e0904dc8cf1b7f42bc34d346"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a></div><div class="ttdeci">void ApplyBias(std::vector&lt; T &gt; &amp;v, float vScale, int32_t vOffset, const std::vector&lt; B &gt; &amp;bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00169">Conv2dTestImpl.cpp:169</a></div></div>
3415<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div>
3416<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>
3417<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::DepthwiseConvolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00192">WorkloadData.hpp:192</a></div></div>
3418<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div>
3419<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>
3420<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::DepthwiseConvolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation factor value for height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00488">Descriptors.hpp:488</a></div></div>
3421<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::DepthwiseConvolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00191">WorkloadData.hpp:191</a></div></div>
3422<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div>
3423<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</a></div></div>
3424<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::DepthwiseConvolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation factor value for width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00486">Descriptors.hpp:486</a></div></div>
3425<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div>
3426<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div>
3427<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</a></div></div>
3428<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>
3429<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div>
3430<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
3431<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>
3432<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>
3433<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>
3434<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
3435<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div>
3436<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
3437<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div>
3438<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>
3439<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>
3440<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div>
3441<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_accb9759dfd2880efe0f8d2705ddee448"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">armnn::IWorkloadFactory::CreateDepthwiseConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01177">WorkloadFactory.cpp:1177</a></div></div>
3442<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00183">WorkloadData.hpp:183</a></div></div>
3443<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
3444<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>
3445<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div>
3446</div><!-- fragment -->
3447</div>
3448</div>
3449<a id="a8076c31bd6e9eae629994a89a5fa18c3"></a>
3450<h2 class="memtitle"><span class="permalink"><a href="#a8076c31bd6e9eae629994a89a5fa18c3">&#9670;&nbsp;</a></span>DepthwiseConvolution2dUint8Test()</h2>
3451
3452<div class="memitem">
3453<div class="memproto">
3454 <table class="memname">
3455 <tr>
3456 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; DepthwiseConvolution2dUint8Test </td>
3457 <td>(</td>
3458 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3459 <td class="paramname"><em>workloadFactory</em>, </td>
3460 </tr>
3461 <tr>
3462 <td class="paramkey"></td>
3463 <td></td>
3464 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3465 <td class="paramname"><em>memoryManager</em>, </td>
3466 </tr>
3467 <tr>
3468 <td class="paramkey"></td>
3469 <td></td>
3470 <td class="paramtype">bool&#160;</td>
3471 <td class="paramname"><em>biasEnabled</em>, </td>
3472 </tr>
3473 <tr>
3474 <td class="paramkey"></td>
3475 <td></td>
3476 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3477 <td class="paramname"><em>layout</em>&#160;</td>
3478 </tr>
3479 <tr>
3480 <td></td>
3481 <td>)</td>
3482 <td></td><td></td>
3483 </tr>
3484 </table>
3485</div><div class="memdoc">
3486
3487<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03264">3264</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3488<div class="fragment"><div class="line"><a name="l03269"></a><span class="lineno"> 3269</span>&#160;{</div><div class="line"><a name="l03270"></a><span class="lineno"> 3270</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl&lt;armnn::DataType::QAsymmU8, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03271"></a><span class="lineno"> 3271</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03272"></a><span class="lineno"> 3272</span>&#160;}</div></div><!-- fragment -->
3489</div>
3490</div>
3491<a id="a3481304dfd3e941b809c64979b940ad5"></a>
3492<h2 class="memtitle"><span class="permalink"><a href="#a3481304dfd3e941b809c64979b940ad5">&#9670;&nbsp;</a></span>GetBias()</h2>
3493
3494<div class="memitem">
3495<div class="memproto">
3496 <table class="memname">
3497 <tr>
3498 <td class="memname">boost::multi_array&lt;T, 1&gt; GetBias </td>
3499 <td>(</td>
3500 <td class="paramtype">bool&#160;</td>
3501 <td class="paramname"><em>biasEnabled</em>, </td>
3502 </tr>
3503 <tr>
3504 <td class="paramkey"></td>
3505 <td></td>
3506 <td class="paramtype">float&#160;</td>
3507 <td class="paramname"><em>qScale</em>, </td>
3508 </tr>
3509 <tr>
3510 <td class="paramkey"></td>
3511 <td></td>
3512 <td class="paramtype"><a class="el" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&#160;</td>
3513 <td class="paramname"><em>outputInfo</em>, </td>
3514 </tr>
3515 <tr>
3516 <td class="paramkey"></td>
3517 <td></td>
3518 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3519 <td class="paramname"><em>layout</em>&#160;</td>
3520 </tr>
3521 <tr>
3522 <td></td>
3523 <td>)</td>
3524 <td></td><td></td>
3525 </tr>
3526 </table>
3527</div><div class="memdoc">
3528
3529<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00122">122</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3530
3531<p class="reference">References <a class="el" href="_data_layout_indexed_8hpp_source.xhtml#l00023">DataLayoutIndexed::GetChannelsIndex()</a>, and <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>.</p>
3532<div class="fragment"><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160;{</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> dataLayoutIndexed(layout);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelsIndex = dataLayoutIndexed.GetChannelsIndex();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[channelsIndex];</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; <span class="keywordflow">switch</span> (outputChannels)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; <span class="keywordflow">default</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="keywordflow">return</span> GetBias2&lt;ArmnnType&gt;(biasEnabled, qScale);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keywordflow">case</span> 4:</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <span class="keywordflow">return</span> GetBias4&lt;ArmnnType&gt;(biasEnabled, qScale);</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; <span class="keywordflow">case</span> 8:</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; <span class="keywordflow">return</span> GetBias8&lt;ArmnnType&gt;(biasEnabled, qScale);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; }</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;}</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>
3533<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div>
3534</div><!-- fragment -->
3535</div>
3536</div>
3537<a id="ad80bc46727797692d35f94d5935469cb"></a>
3538<h2 class="memtitle"><span class="permalink"><a href="#ad80bc46727797692d35f94d5935469cb">&#9670;&nbsp;</a></span>GetBias2()</h2>
3539
3540<div class="memitem">
3541<div class="memproto">
3542 <table class="memname">
3543 <tr>
3544 <td class="memname">boost::multi_array&lt;T, 1&gt; GetBias2 </td>
3545 <td>(</td>
3546 <td class="paramtype">bool&#160;</td>
3547 <td class="paramname"><em>biasEnabled</em>, </td>
3548 </tr>
3549 <tr>
3550 <td class="paramkey"></td>
3551 <td></td>
3552 <td class="paramtype">float&#160;</td>
3553 <td class="paramname"><em>qScale</em>&#160;</td>
3554 </tr>
3555 <tr>
3556 <td></td>
3557 <td>)</td>
3558 <td></td><td></td>
3559 </tr>
3560 </table>
3561</div><div class="memdoc">
3562
3563<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00074">74</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3564<div class="fragment"><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;{</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(Bias2.size())}, ArmnnType);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; boost::multi_array&lt;T, 1&gt; bias = MakeTensor&lt;T, 1&gt;(biasDesc, QuantizedVector&lt;T&gt;(Bias2, qScale, 0.0f));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="keywordflow">return</span> bias;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; {</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="keywordflow">return</span> boost::multi_array&lt;T, 1&gt;();</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; }</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</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>
3565</div><!-- fragment -->
3566</div>
3567</div>
3568<a id="aa794621b8665d1df93a1c9aa95d5a90d"></a>
3569<h2 class="memtitle"><span class="permalink"><a href="#aa794621b8665d1df93a1c9aa95d5a90d">&#9670;&nbsp;</a></span>GetBias4()</h2>
3570
3571<div class="memitem">
3572<div class="memproto">
3573 <table class="memname">
3574 <tr>
3575 <td class="memname">boost::multi_array&lt;T, 1&gt; GetBias4 </td>
3576 <td>(</td>
3577 <td class="paramtype">bool&#160;</td>
3578 <td class="paramname"><em>biasEnabled</em>, </td>
3579 </tr>
3580 <tr>
3581 <td class="paramkey"></td>
3582 <td></td>
3583 <td class="paramtype">float&#160;</td>
3584 <td class="paramname"><em>qScale</em>&#160;</td>
3585 </tr>
3586 <tr>
3587 <td></td>
3588 <td>)</td>
3589 <td></td><td></td>
3590 </tr>
3591 </table>
3592</div><div class="memdoc">
3593
3594<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00090">90</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3595<div class="fragment"><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160;{</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(Bias4.size())}, ArmnnType);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; boost::multi_array&lt;T, 1&gt; bias = MakeTensor&lt;T, 1&gt;(biasDesc, QuantizedVector&lt;T&gt;(Bias4, qScale, 0.0f));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="keywordflow">return</span> bias;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="keywordflow">else</span></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">return</span> boost::multi_array&lt;T, 1&gt;();</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;}</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>
3596</div><!-- fragment -->
3597</div>
3598</div>
3599<a id="ae04bff4e44deed6908feae29e57ffe0c"></a>
3600<h2 class="memtitle"><span class="permalink"><a href="#ae04bff4e44deed6908feae29e57ffe0c">&#9670;&nbsp;</a></span>GetBias8()</h2>
3601
3602<div class="memitem">
3603<div class="memproto">
3604 <table class="memname">
3605 <tr>
3606 <td class="memname">boost::multi_array&lt;T, 1&gt; GetBias8 </td>
3607 <td>(</td>
3608 <td class="paramtype">bool&#160;</td>
3609 <td class="paramname"><em>biasEnabled</em>, </td>
3610 </tr>
3611 <tr>
3612 <td class="paramkey"></td>
3613 <td></td>
3614 <td class="paramtype">float&#160;</td>
3615 <td class="paramname"><em>qScale</em>&#160;</td>
3616 </tr>
3617 <tr>
3618 <td></td>
3619 <td>)</td>
3620 <td></td><td></td>
3621 </tr>
3622 </table>
3623</div><div class="memdoc">
3624
3625<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00106">106</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3626<div class="fragment"><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160;{</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(Bias4.size())}, ArmnnType);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; boost::multi_array&lt;T, 1&gt; bias = MakeTensor&lt;T, 1&gt;(biasDesc, QuantizedVector&lt;T&gt;(Bias8, qScale, 0.0f));</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keywordflow">return</span> bias;</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; <span class="keywordflow">return</span> boost::multi_array&lt;T, 1&gt;();</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</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>
3627</div><!-- fragment -->
3628</div>
3629</div>
3630<a id="ac7bae01fdca8edac70cc9bc722426b17"></a>
3631<h2 class="memtitle"><span class="permalink"><a href="#ac7bae01fdca8edac70cc9bc722426b17">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3NhwcTest()</h2>
3632
3633<div class="memitem">
3634<div class="memproto">
3635 <table class="memname">
3636 <tr>
3637 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; SimpleConvolution2d3x3NhwcTest </td>
3638 <td>(</td>
3639 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3640 <td class="paramname"><em>workloadFactory</em>, </td>
3641 </tr>
3642 <tr>
3643 <td class="paramkey"></td>
3644 <td></td>
3645 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3646 <td class="paramname"><em>memoryManager</em>, </td>
3647 </tr>
3648 <tr>
3649 <td class="paramkey"></td>
3650 <td></td>
3651 <td class="paramtype">bool&#160;</td>
3652 <td class="paramname"><em>biasEnabled</em>&#160;</td>
3653 </tr>
3654 <tr>
3655 <td></td>
3656 <td>)</td>
3657 <td></td><td></td>
3658 </tr>
3659 </table>
3660</div><div class="memdoc">
3661
3662<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02996">2996</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3663
3664<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p>
3665<div class="fragment"><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>&#160;{</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x3NhwcTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>&#160; workloadFactory,</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>&#160; memoryManager,</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span>&#160; 0.f,</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span>&#160; 0,</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span>&#160; biasEnabled,</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l03008"></a><span class="lineno"> 3008</span>&#160;}</div><div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
3666</div><!-- fragment -->
3667</div>
3668</div>
3669<a id="a8225effadfc56a5d831ae0f7f686a6cf"></a>
3670<h2 class="memtitle"><span class="permalink"><a href="#a8225effadfc56a5d831ae0f7f686a6cf">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3NhwcTestCommon()</h2>
3671
3672<div class="memitem">
3673<div class="memproto">
3674 <table class="memname">
3675 <tr>
3676 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleConvolution2d3x3NhwcTestCommon </td>
3677 <td>(</td>
3678 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3679 <td class="paramname"><em>workloadFactory</em>, </td>
3680 </tr>
3681 <tr>
3682 <td class="paramkey"></td>
3683 <td></td>
3684 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3685 <td class="paramname"><em>memoryManager</em>, </td>
3686 </tr>
3687 <tr>
3688 <td class="paramkey"></td>
3689 <td></td>
3690 <td class="paramtype">float&#160;</td>
3691 <td class="paramname"><em>qScale</em>, </td>
3692 </tr>
3693 <tr>
3694 <td class="paramkey"></td>
3695 <td></td>
3696 <td class="paramtype">int32_t&#160;</td>
3697 <td class="paramname"><em>qOffset</em>, </td>
3698 </tr>
3699 <tr>
3700 <td class="paramkey"></td>
3701 <td></td>
3702 <td class="paramtype">bool&#160;</td>
3703 <td class="paramname"><em>biasEnabled</em>, </td>
3704 </tr>
3705 <tr>
3706 <td class="paramkey"></td>
3707 <td></td>
3708 <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3709 <td class="paramname"><em>dataLayout</em>&#160;</td>
3710 </tr>
3711 <tr>
3712 <td></td>
3713 <td>)</td>
3714 <td></td><td></td>
3715 </tr>
3716 </table>
3717</div><div class="memdoc">
3718
3719<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00582">582</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3720
3721<p class="reference">References <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>.</p>
3722<div class="fragment"><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>&#160;{</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(biasEnabled);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>&#160; <span class="comment">// Use common single-batch 5x5 image.</span></div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>&#160;</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 3, 4, 1}, ArmnnType);</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>&#160; boost::multi_array&lt;T, 4&gt; input = MakeTensor&lt;T, 4&gt;(inputDesc,</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>&#160; {</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>&#160; 1, 5, 2, 3,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>&#160; 8, 7, 3, 6,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>&#160; 3, 3, 9, 1</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;</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>&#160;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>&#160; <span class="comment">// Use a 2-element batch of 3-channel 3x3 kernels.</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({1, 3, 3, 1}, ArmnnType);</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>&#160; boost::multi_array&lt;T, 4&gt; kernel = MakeTensor&lt;T, 4&gt;(kernelDesc, {</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>&#160; 4, 5, 6,</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>&#160; 3, 2, 1</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>&#160; });</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>&#160;</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>&#160; <span class="comment">// Expected output is 1 batch of a 5x5 image.</span></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 3, 4, 1}, ArmnnType);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>&#160;</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; outputData =</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>&#160; {</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>&#160; 23, 41, 33, 21,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>&#160; 44, 65, 76, 52,</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>&#160; 82, 85, 79, 42</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>&#160; };</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>&#160;</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputDesc, outputData);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>&#160;</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2dNhwcTestImpl&lt;ArmnnType, ArmnnType&gt;(</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>&#160; workloadFactory,</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>&#160; memoryManager,</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>&#160; input,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>&#160; kernel,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>&#160; boost::multi_array&lt;T, 1&gt;(),</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>&#160; expectedOutput,</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>&#160; dataLayout,</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>&#160; qScale,</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>&#160; qOffset);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</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>
3723<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>
3724</div><!-- fragment -->
3725</div>
3726</div>
3727<a id="abac8f73ae590a93fe91115371ae4ced3"></a>
3728<h2 class="memtitle"><span class="permalink"><a href="#abac8f73ae590a93fe91115371ae4ced3">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3QSymm16Test()</h2>
3729
3730<div class="memitem">
3731<div class="memproto">
3732 <table class="memname">
3733 <tr>
3734 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;int16_t, 4&gt; SimpleConvolution2d3x3QSymm16Test </td>
3735 <td>(</td>
3736 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3737 <td class="paramname"><em>workloadFactory</em>, </td>
3738 </tr>
3739 <tr>
3740 <td class="paramkey"></td>
3741 <td></td>
3742 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3743 <td class="paramname"><em>memoryManager</em>, </td>
3744 </tr>
3745 <tr>
3746 <td class="paramkey"></td>
3747 <td></td>
3748 <td class="paramtype">bool&#160;</td>
3749 <td class="paramname"><em>biasEnabled</em>, </td>
3750 </tr>
3751 <tr>
3752 <td class="paramkey"></td>
3753 <td></td>
3754 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3755 <td class="paramname"><em>layout</em>&#160;</td>
3756 </tr>
3757 <tr>
3758 <td></td>
3759 <td>)</td>
3760 <td></td><td></td>
3761 </tr>
3762 </table>
3763</div><div class="memdoc">
3764
3765<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03045">3045</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3766<div class="fragment"><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>&#160;{</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x3TestCommon&lt;armnn::DataType::QSymmS16, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span>&#160;}</div></div><!-- fragment -->
3767</div>
3768</div>
3769<a id="af4ac6874d18e1cb59873a17073512873"></a>
3770<h2 class="memtitle"><span class="permalink"><a href="#af4ac6874d18e1cb59873a17073512873">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3Stride2x2Test()</h2>
3771
3772<div class="memitem">
3773<div class="memproto">
3774 <table class="memname">
3775 <tr>
3776 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; SimpleConvolution2d3x3Stride2x2Test </td>
3777 <td>(</td>
3778 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3779 <td class="paramname"><em>workloadFactory</em>, </td>
3780 </tr>
3781 <tr>
3782 <td class="paramkey"></td>
3783 <td></td>
3784 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3785 <td class="paramname"><em>memoryManager</em>, </td>
3786 </tr>
3787 <tr>
3788 <td class="paramkey"></td>
3789 <td></td>
3790 <td class="paramtype">bool&#160;</td>
3791 <td class="paramname"><em>biasEnabled</em>, </td>
3792 </tr>
3793 <tr>
3794 <td class="paramkey"></td>
3795 <td></td>
3796 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3797 <td class="paramname"><em>layout</em>&#160;</td>
3798 </tr>
3799 <tr>
3800 <td></td>
3801 <td>)</td>
3802 <td></td><td></td>
3803 </tr>
3804 </table>
3805</div><div class="memdoc">
3806
3807<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03010">3010</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3808<div class="fragment"><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span>&#160;{</div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x3Stride2x2TestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span>&#160; workloadFactory,</div><div class="line"><a name="l03018"></a><span class="lineno"> 3018</span>&#160; memoryManager,</div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>&#160; 0.f,</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>&#160; 0,</div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span>&#160; biasEnabled,</div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>&#160; layout);</div><div class="line"><a name="l03023"></a><span class="lineno"> 3023</span>&#160;}</div></div><!-- fragment -->
3809</div>
3810</div>
3811<a id="aafa5b575d2bc27ec7229f1d87ab8efdb"></a>
3812<h2 class="memtitle"><span class="permalink"><a href="#aafa5b575d2bc27ec7229f1d87ab8efdb">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3Stride2x2TestCommon()</h2>
3813
3814<div class="memitem">
3815<div class="memproto">
3816 <table class="memname">
3817 <tr>
3818 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleConvolution2d3x3Stride2x2TestCommon </td>
3819 <td>(</td>
3820 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3821 <td class="paramname"><em>workloadFactory</em>, </td>
3822 </tr>
3823 <tr>
3824 <td class="paramkey"></td>
3825 <td></td>
3826 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3827 <td class="paramname"><em>memoryManager</em>, </td>
3828 </tr>
3829 <tr>
3830 <td class="paramkey"></td>
3831 <td></td>
3832 <td class="paramtype">float&#160;</td>
3833 <td class="paramname"><em>qScale</em>, </td>
3834 </tr>
3835 <tr>
3836 <td class="paramkey"></td>
3837 <td></td>
3838 <td class="paramtype">int32_t&#160;</td>
3839 <td class="paramname"><em>qOffset</em>, </td>
3840 </tr>
3841 <tr>
3842 <td class="paramkey"></td>
3843 <td></td>
3844 <td class="paramtype">bool&#160;</td>
3845 <td class="paramname"><em>biasEnabled</em>, </td>
3846 </tr>
3847 <tr>
3848 <td class="paramkey"></td>
3849 <td></td>
3850 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> &amp;&#160;</td>
3851 <td class="paramname"><em>dataLayout</em>&#160;</td>
3852 </tr>
3853 <tr>
3854 <td></td>
3855 <td>)</td>
3856 <td></td><td></td>
3857 </tr>
3858 </table>
3859</div><div class="memdoc">
3860
3861<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00635">635</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3862
3863<p class="reference">References <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>.</p>
3864<div class="fragment"><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>&#160;{</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(biasEnabled);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>&#160;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>&#160; <span class="comment">// Input is a single-batch, 1 channel, 5x5 image.</span></div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 5, 5, 1}, ArmnnType);</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>&#160; boost::multi_array&lt;T, 4&gt; input = MakeTensor&lt;T, 4&gt;(inputDesc,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>&#160; {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>&#160; 1, 5, 2, 3, 5,</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>&#160; 8, 7, 3, 6, 3,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>&#160; 3, 3, 9, 1, 9,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>&#160; 4, 1, 8, 1, 3,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>&#160; 6, 8, 1, 9, 2</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>&#160; });</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>&#160;</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>&#160; <span class="comment">// Use a 3x3 kernel.</span></div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({1, 3, 3, 1}, ArmnnType);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>&#160; boost::multi_array&lt;T, 4&gt; kernel = MakeTensor&lt;T, 4&gt;(kernelDesc,</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; 4, 5, 6,</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>&#160; 3, 2, 1</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>&#160; });</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; <span class="comment">// Expected output is a single-batch, 1 channel, 3x3 image.</span></div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 3, 3, 1}, ArmnnType);</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="keyword">const</span> std::vector&lt;T&gt; outputData =</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>&#160; {</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>&#160; 23, 33, 24,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>&#160; 91, 99, 48,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>&#160; 26, 50, 19</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;</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputDesc, outputData);</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>&#160;</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>&#160; uint32_t padLeft = 1;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>&#160; uint32_t padTop = 1;</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>&#160; uint32_t padRight = 1;</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>&#160; uint32_t padBottom = 1;</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>&#160; uint32_t strideX = 2;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>&#160; uint32_t strideY = 2;</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>&#160;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2dNhwcTestImpl&lt;ArmnnType, ArmnnType&gt;(</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>&#160; workloadFactory,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>&#160; memoryManager,</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>&#160; input,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>&#160; kernel,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>&#160; boost::multi_array&lt;T, 1&gt;(),</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>&#160; expectedOutput,</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>&#160; dataLayout,</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>&#160; qScale,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>&#160; qOffset,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>&#160; padLeft,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>&#160; padTop,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>&#160; padRight,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>&#160; padBottom,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>&#160; strideX,</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>&#160; strideY);</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</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>
3865<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>
3866</div><!-- fragment -->
3867</div>
3868</div>
3869<a id="acbe1a2adccd9e0aad14fc0ccb9266b0d"></a>
3870<h2 class="memtitle"><span class="permalink"><a href="#acbe1a2adccd9e0aad14fc0ccb9266b0d">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3Test()</h2>
3871
3872<div class="memitem">
3873<div class="memproto">
3874 <table class="memname">
3875 <tr>
3876 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; SimpleConvolution2d3x3Test </td>
3877 <td>(</td>
3878 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3879 <td class="paramname"><em>workloadFactory</em>, </td>
3880 </tr>
3881 <tr>
3882 <td class="paramkey"></td>
3883 <td></td>
3884 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3885 <td class="paramname"><em>memoryManager</em>, </td>
3886 </tr>
3887 <tr>
3888 <td class="paramkey"></td>
3889 <td></td>
3890 <td class="paramtype">bool&#160;</td>
3891 <td class="paramname"><em>biasEnabled</em>, </td>
3892 </tr>
3893 <tr>
3894 <td class="paramkey"></td>
3895 <td></td>
3896 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3897 <td class="paramname"><em>layout</em>&#160;</td>
3898 </tr>
3899 <tr>
3900 <td></td>
3901 <td>)</td>
3902 <td></td><td></td>
3903 </tr>
3904 </table>
3905</div><div class="memdoc">
3906
3907<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02986">2986</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3908<div class="fragment"><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span>&#160;{</div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x3TestCommon&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span>&#160; workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>&#160;}</div></div><!-- fragment -->
3909</div>
3910</div>
3911<a id="a5070a9bac7ba582ed116a8b2323ed2a5"></a>
3912<h2 class="memtitle"><span class="permalink"><a href="#a5070a9bac7ba582ed116a8b2323ed2a5">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3TestCommon()</h2>
3913
3914<div class="memitem">
3915<div class="memproto">
3916 <table class="memname">
3917 <tr>
3918 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleConvolution2d3x3TestCommon </td>
3919 <td>(</td>
3920 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3921 <td class="paramname"><em>workloadFactory</em>, </td>
3922 </tr>
3923 <tr>
3924 <td class="paramkey"></td>
3925 <td></td>
3926 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3927 <td class="paramname"><em>memoryManager</em>, </td>
3928 </tr>
3929 <tr>
3930 <td class="paramkey"></td>
3931 <td></td>
3932 <td class="paramtype">float&#160;</td>
3933 <td class="paramname"><em>qScale</em>, </td>
3934 </tr>
3935 <tr>
3936 <td class="paramkey"></td>
3937 <td></td>
3938 <td class="paramtype">int32_t&#160;</td>
3939 <td class="paramname"><em>qOffset</em>, </td>
3940 </tr>
3941 <tr>
3942 <td class="paramkey"></td>
3943 <td></td>
3944 <td class="paramtype">bool&#160;</td>
3945 <td class="paramname"><em>biasEnabled</em>, </td>
3946 </tr>
3947 <tr>
3948 <td class="paramkey"></td>
3949 <td></td>
3950 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3951 <td class="paramname"><em>layout</em>&#160;</td>
3952 </tr>
3953 <tr>
3954 <td></td>
3955 <td>)</td>
3956 <td></td><td></td>
3957 </tr>
3958 </table>
3959</div><div class="memdoc">
3960
3961<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00790">790</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
3962<div class="fragment"><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>&#160;{</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>&#160; <span class="comment">// Use a 3x3 kernel, which exercises ArmCompute&#39;s direct convolution path.</span></div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>&#160;</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>&#160; <span class="comment">// Use common single-batch 3-channel 16x8 image.</span></div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 3, 8, 16}, ArmnnType);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>&#160; boost::multi_array&lt;T, 4&gt; input = MakeTensor&lt;T, 4&gt;(inputDesc, QuantizedVector&lt;T&gt;(ConvInput3x8x16, qScale, qOffset));</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; <span class="comment">// Use a 2-element batch of 3-channel 3x3 kernels.</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({2, 3, 3, 3}, ArmnnType);</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>&#160; boost::multi_array&lt;T, 4&gt; kernel = MakeTensor&lt;T, 4&gt;(kernelDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>&#160; 1, -1, 1,</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>&#160;</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>&#160;</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>&#160; 2, 2, 2,</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>&#160; 2, 2, 2,</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>&#160; 2, 2, 2,</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;</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>&#160; 0, 0, 0,</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; 1, 1, 1,</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>&#160;</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>&#160; 0, 0, 0</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>&#160; },</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>&#160; qScale, qOffset)));</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>&#160;</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>&#160; <span class="comment">// Expected output is 1 batch of a 2-channel 14x6 image.</span></div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 2, 6, 14}, ArmnnType);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>&#160; -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15,</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>&#160; -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16,</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>&#160; -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>&#160; -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>&#160; -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>&#160; -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</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; 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>&#160; 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>&#160; 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>&#160; 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>&#160; 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>&#160; 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>&#160; },</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>&#160; qScale, qOffset)));</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="keywordflow">return</span> SimpleConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>&#160; workloadFactory,</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>&#160; memoryManager,</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>&#160; input,</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>&#160; kernel,</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>&#160; GetBias2&lt;ArmnnBType&gt;(biasEnabled, qScale * qScale),</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>&#160; expectedOutput,</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>&#160; qScale,</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>&#160; qOffset,</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>&#160; layout);</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</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>
3963</div><!-- fragment -->
3964</div>
3965</div>
3966<a id="ad45f359d9d4bee360bee857faa79d292"></a>
3967<h2 class="memtitle"><span class="permalink"><a href="#ad45f359d9d4bee360bee857faa79d292">&#9670;&nbsp;</a></span>SimpleConvolution2d3x3Uint8Test()</h2>
3968
3969<div class="memitem">
3970<div class="memproto">
3971 <table class="memname">
3972 <tr>
3973 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; SimpleConvolution2d3x3Uint8Test </td>
3974 <td>(</td>
3975 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
3976 <td class="paramname"><em>workloadFactory</em>, </td>
3977 </tr>
3978 <tr>
3979 <td class="paramkey"></td>
3980 <td></td>
3981 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
3982 <td class="paramname"><em>memoryManager</em>, </td>
3983 </tr>
3984 <tr>
3985 <td class="paramkey"></td>
3986 <td></td>
3987 <td class="paramtype">bool&#160;</td>
3988 <td class="paramname"><em>biasEnabled</em>, </td>
3989 </tr>
3990 <tr>
3991 <td class="paramkey"></td>
3992 <td></td>
3993 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
3994 <td class="paramname"><em>layout</em>&#160;</td>
3995 </tr>
3996 <tr>
3997 <td></td>
3998 <td>)</td>
3999 <td></td><td></td>
4000 </tr>
4001 </table>
4002</div><div class="memdoc">
4003
4004<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03025">3025</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4005<div class="fragment"><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>&#160;{</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x3TestCommon&lt;armnn::DataType::QAsymmU8, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span>&#160;}</div></div><!-- fragment -->
4006</div>
4007</div>
4008<a id="a9dcd2fb98f5c3284c74f65a7c7a69da1"></a>
4009<h2 class="memtitle"><span class="permalink"><a href="#a9dcd2fb98f5c3284c74f65a7c7a69da1">&#9670;&nbsp;</a></span>SimpleConvolution2d3x5QSymm16Test()</h2>
4010
4011<div class="memitem">
4012<div class="memproto">
4013 <table class="memname">
4014 <tr>
4015 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;int16_t, 4&gt; SimpleConvolution2d3x5QSymm16Test </td>
4016 <td>(</td>
4017 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4018 <td class="paramname"><em>workloadFactory</em>, </td>
4019 </tr>
4020 <tr>
4021 <td class="paramkey"></td>
4022 <td></td>
4023 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4024 <td class="paramname"><em>memoryManager</em>, </td>
4025 </tr>
4026 <tr>
4027 <td class="paramkey"></td>
4028 <td></td>
4029 <td class="paramtype">bool&#160;</td>
4030 <td class="paramname"><em>biasEnabled</em>, </td>
4031 </tr>
4032 <tr>
4033 <td class="paramkey"></td>
4034 <td></td>
4035 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
4036 <td class="paramname"><em>layout</em>&#160;</td>
4037 </tr>
4038 <tr>
4039 <td></td>
4040 <td>)</td>
4041 <td></td><td></td>
4042 </tr>
4043 </table>
4044</div><div class="memdoc">
4045
4046<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03035">3035</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4047<div class="fragment"><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span>&#160;{</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x5TestCommon&lt;armnn::DataType::QSymmS16, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>&#160;}</div></div><!-- fragment -->
4048</div>
4049</div>
4050<a id="afb5e7d86e241292d9cb899b960da54af"></a>
4051<h2 class="memtitle"><span class="permalink"><a href="#afb5e7d86e241292d9cb899b960da54af">&#9670;&nbsp;</a></span>SimpleConvolution2d3x5Test()</h2>
4052
4053<div class="memitem">
4054<div class="memproto">
4055 <table class="memname">
4056 <tr>
4057 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; SimpleConvolution2d3x5Test </td>
4058 <td>(</td>
4059 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4060 <td class="paramname"><em>workloadFactory</em>, </td>
4061 </tr>
4062 <tr>
4063 <td class="paramkey"></td>
4064 <td></td>
4065 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4066 <td class="paramname"><em>memoryManager</em>, </td>
4067 </tr>
4068 <tr>
4069 <td class="paramkey"></td>
4070 <td></td>
4071 <td class="paramtype">bool&#160;</td>
4072 <td class="paramname"><em>biasEnabled</em>, </td>
4073 </tr>
4074 <tr>
4075 <td class="paramkey"></td>
4076 <td></td>
4077 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
4078 <td class="paramname"><em>layout</em>&#160;</td>
4079 </tr>
4080 <tr>
4081 <td></td>
4082 <td>)</td>
4083 <td></td><td></td>
4084 </tr>
4085 </table>
4086</div><div class="memdoc">
4087
4088<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02966">2966</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4089<div class="fragment"><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>&#160;{</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x5TestCommon&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>&#160; workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>&#160;}</div></div><!-- fragment -->
4090</div>
4091</div>
4092<a id="a3660079f1e20e5b1618402dfc5214441"></a>
4093<h2 class="memtitle"><span class="permalink"><a href="#a3660079f1e20e5b1618402dfc5214441">&#9670;&nbsp;</a></span>SimpleConvolution2d3x5TestCommon()</h2>
4094
4095<div class="memitem">
4096<div class="memproto">
4097 <table class="memname">
4098 <tr>
4099 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleConvolution2d3x5TestCommon </td>
4100 <td>(</td>
4101 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4102 <td class="paramname"><em>workloadFactory</em>, </td>
4103 </tr>
4104 <tr>
4105 <td class="paramkey"></td>
4106 <td></td>
4107 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4108 <td class="paramname"><em>memoryManager</em>, </td>
4109 </tr>
4110 <tr>
4111 <td class="paramkey"></td>
4112 <td></td>
4113 <td class="paramtype">float&#160;</td>
4114 <td class="paramname"><em>qScale</em>, </td>
4115 </tr>
4116 <tr>
4117 <td class="paramkey"></td>
4118 <td></td>
4119 <td class="paramtype">int32_t&#160;</td>
4120 <td class="paramname"><em>qOffset</em>, </td>
4121 </tr>
4122 <tr>
4123 <td class="paramkey"></td>
4124 <td></td>
4125 <td class="paramtype">bool&#160;</td>
4126 <td class="paramname"><em>biasEnabled</em>, </td>
4127 </tr>
4128 <tr>
4129 <td class="paramkey"></td>
4130 <td></td>
4131 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
4132 <td class="paramname"><em>layout</em>&#160;</td>
4133 </tr>
4134 <tr>
4135 <td></td>
4136 <td>)</td>
4137 <td></td><td></td>
4138 </tr>
4139 </table>
4140</div><div class="memdoc">
4141
4142<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00703">703</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4143<div class="fragment"><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="comment">// Use common single-batch 3-channel 16x8 image.</span></div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 3, 8, 16}, ArmnnType);</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>&#160; boost::multi_array&lt;T, 4&gt; input = MakeTensor&lt;T, 4&gt;(inputDesc, QuantizedVector&lt;T&gt;(ConvInput3x8x16, qScale, qOffset));</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>&#160;</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>&#160; <span class="comment">// Use a 2-element batch with 3-channel 3x5 kernels.</span></div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({2, 3, 5, 3}, ArmnnType);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>&#160; boost::multi_array&lt;T, 4&gt; kernel = MakeTensor&lt;T, 4&gt;(kernelDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>&#160; 1, -1, 1,</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>&#160;</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>&#160; 0, 0, 0,</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; 2, 2, 2,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>&#160; 2, 2, 2,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>&#160; 2, 2, 2,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>&#160; 2, 2, 2,</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>&#160; 2, 2, 2,</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;</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>&#160;</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>&#160; 1, 1, 1,</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>&#160; 1, 1, 1,</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; 0, 0, 0,</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>&#160; 0, 0, 0,</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>&#160; 0, 0, 0</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; qScale, qOffset)));</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>&#160;</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>&#160; <span class="comment">// Expected output is 2 batch elements of a 1-channel 14x4 image.</span></div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 2, 4, 14}, ArmnnType);</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>&#160; -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>&#160; -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>&#160; -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>&#160; -23.5f, -23.5f, -23.5f,</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>&#160; -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>&#160; -23.5f, -23.5f, -23.5f,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>&#160;</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>&#160; 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>&#160; 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>&#160; 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>&#160; 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>&#160; },</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>&#160; qScale, qOffset)));</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>&#160;</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>&#160; workloadFactory,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>&#160; memoryManager,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>&#160; input,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>&#160; kernel,</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>&#160; GetBias2&lt;ArmnnBType&gt;(biasEnabled, qScale * qScale),</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>&#160; expectedOutput,</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>&#160; qScale,</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>&#160; qOffset,</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>&#160; layout);</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</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>
4144</div><!-- fragment -->
4145</div>
4146</div>
4147<a id="a8ffca1c4b38a68b10ba06f4f1416660f"></a>
4148<h2 class="memtitle"><span class="permalink"><a href="#a8ffca1c4b38a68b10ba06f4f1416660f">&#9670;&nbsp;</a></span>SimpleConvolution2d3x5Uint8Test()</h2>
4149
4150<div class="memitem">
4151<div class="memproto">
4152 <table class="memname">
4153 <tr>
4154 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;uint8_t, 4&gt; SimpleConvolution2d3x5Uint8Test </td>
4155 <td>(</td>
4156 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4157 <td class="paramname"><em>workloadFactory</em>, </td>
4158 </tr>
4159 <tr>
4160 <td class="paramkey"></td>
4161 <td></td>
4162 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4163 <td class="paramname"><em>memoryManager</em>, </td>
4164 </tr>
4165 <tr>
4166 <td class="paramkey"></td>
4167 <td></td>
4168 <td class="paramtype">bool&#160;</td>
4169 <td class="paramname"><em>biasEnabled</em>, </td>
4170 </tr>
4171 <tr>
4172 <td class="paramkey"></td>
4173 <td></td>
4174 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
4175 <td class="paramname"><em>layout</em>&#160;</td>
4176 </tr>
4177 <tr>
4178 <td></td>
4179 <td>)</td>
4180 <td></td><td></td>
4181 </tr>
4182 </table>
4183</div><div class="memdoc">
4184
4185<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02976">2976</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4186<div class="fragment"><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span>&#160;{</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>&#160; <span class="keywordflow">return</span> SimpleConvolution2d3x5TestCommon&lt;armnn::DataType::QAsymmU8, armnn::DataType::Signed32&gt;(</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span>&#160; workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span>&#160;}</div></div><!-- fragment -->
4187</div>
4188</div>
4189<a id="af32b0642214e3129d8e93fa45a12e704"></a>
4190<h2 class="memtitle"><span class="permalink"><a href="#af32b0642214e3129d8e93fa45a12e704">&#9670;&nbsp;</a></span>SimpleConvolution2dAsymmetricPaddingTestCommon()</h2>
4191
4192<div class="memitem">
4193<div class="memproto">
4194 <table class="memname">
4195 <tr>
4196 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleConvolution2dAsymmetricPaddingTestCommon </td>
4197 <td>(</td>
4198 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4199 <td class="paramname"><em>workloadFactory</em>, </td>
4200 </tr>
4201 <tr>
4202 <td class="paramkey"></td>
4203 <td></td>
4204 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4205 <td class="paramname"><em>memoryManager</em>, </td>
4206 </tr>
4207 <tr>
4208 <td class="paramkey"></td>
4209 <td></td>
4210 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
4211 <td class="paramname"><em>layout</em>, </td>
4212 </tr>
4213 <tr>
4214 <td class="paramkey"></td>
4215 <td></td>
4216 <td class="paramtype">float&#160;</td>
4217 <td class="paramname"><em>qScale</em>, </td>
4218 </tr>
4219 <tr>
4220 <td class="paramkey"></td>
4221 <td></td>
4222 <td class="paramtype">int32_t&#160;</td>
4223 <td class="paramname"><em>qOffset</em>&#160;</td>
4224 </tr>
4225 <tr>
4226 <td></td>
4227 <td>)</td>
4228 <td></td><td></td>
4229 </tr>
4230 </table>
4231</div><div class="memdoc">
4232
4233<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00936">936</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4234<div class="fragment"><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">// Use a single-batch 1-channel 5x5 image as input.</span></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({ 1, 1, 5, 5 }, ArmnnType);</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>&#160; boost::multi_array&lt;T, 4&gt; input = MakeTensor&lt;T, 4&gt;(inputDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>&#160; 11,21,31,41,51,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>&#160; 12,22,32,42,52,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>&#160; 13,23,33,43,53,</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>&#160; 14,24,34,44,54,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>&#160; 15,25,35,45,55,</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>&#160; }, qScale, qOffset)));</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>&#160;</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>&#160; <span class="comment">// Use 1 batch of a 1-channel 4x4 kernel.</span></div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>&#160; boost::multi_array&lt;T, 4&gt; kernel = MakeTensor&lt;T, 4&gt;(kernelDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>&#160; -11,-21,-31,-41,</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>&#160; -12,-22,-32,-42,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>&#160; -13,-23,-33,-43,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>&#160; -14,-24,-34,-44,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>&#160; },</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>&#160; qScale, qOffset)));</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>&#160;</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>&#160; <span class="comment">// Expected output is 1 batch of a 1-channel 5x5 image.</span></div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({ 1, 1, 5, 5 }, ArmnnType);</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>&#160; std::vector&lt;T&gt; myVec(outputDesc.GetNumElements(), 0);</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputDesc, std::vector&lt;T&gt;(</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>&#160; -7140, -10580, -13940, -9300, -5230,</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>&#160; -9590, -14120, -18520, -12290, -6860,</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>&#160; -9980, -14560, -18960, -12560, -7000,</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>&#160; -7518, -10904, -14144, -9318, -5152,</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>&#160; -5032, -7256, -9376, -6142, -3368,</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>&#160; },</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>&#160; qScale, qOffset)));</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="keywordflow">return</span> SimpleConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>&#160; workloadFactory,</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>&#160; memoryManager,</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>&#160; input,</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>&#160; kernel,</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>&#160; GetBias2&lt;ArmnnBType&gt;(<span class="keyword">false</span>, qScale * qScale),</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>&#160; expectedOutput,</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>&#160; qScale,</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>&#160; qOffset,</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>&#160; layout,</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>&#160; 1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>&#160; 1, <span class="comment">// Padding top.</span></div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>&#160; 2, <span class="comment">// Padding right.</span></div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>&#160; 2); <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</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>
4235</div><!-- fragment -->
4236</div>
4237</div>
4238<a id="ac79e75b3bcb6cb8c34f0bd4e3e35f73e"></a>
4239<h2 class="memtitle"><span class="permalink"><a href="#ac79e75b3bcb6cb8c34f0bd4e3e35f73e">&#9670;&nbsp;</a></span>SimpleConvolution2dNhwcTestImpl()</h2>
4240
4241<div class="memitem">
4242<div class="memproto">
4243 <table class="memname">
4244 <tr>
4245 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleConvolution2dNhwcTestImpl </td>
4246 <td>(</td>
4247 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4248 <td class="paramname"><em>workloadFactory</em>, </td>
4249 </tr>
4250 <tr>
4251 <td class="paramkey"></td>
4252 <td></td>
4253 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4254 <td class="paramname"><em>memoryManager</em>, </td>
4255 </tr>
4256 <tr>
4257 <td class="paramkey"></td>
4258 <td></td>
4259 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
4260 <td class="paramname"><em>input</em>, </td>
4261 </tr>
4262 <tr>
4263 <td class="paramkey"></td>
4264 <td></td>
4265 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
4266 <td class="paramname"><em>kernel</em>, </td>
4267 </tr>
4268 <tr>
4269 <td class="paramkey"></td>
4270 <td></td>
4271 <td class="paramtype">const boost::multi_array&lt; B, 1 &gt; &amp;&#160;</td>
4272 <td class="paramname"><em>bias</em>, </td>
4273 </tr>
4274 <tr>
4275 <td class="paramkey"></td>
4276 <td></td>
4277 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
4278 <td class="paramname"><em>outputExpected</em>, </td>
4279 </tr>
4280 <tr>
4281 <td class="paramkey"></td>
4282 <td></td>
4283 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
4284 <td class="paramname"><em>dataLayout</em>, </td>
4285 </tr>
4286 <tr>
4287 <td class="paramkey"></td>
4288 <td></td>
4289 <td class="paramtype">float&#160;</td>
4290 <td class="paramname"><em>qScale</em>, </td>
4291 </tr>
4292 <tr>
4293 <td class="paramkey"></td>
4294 <td></td>
4295 <td class="paramtype">int32_t&#160;</td>
4296 <td class="paramname"><em>qOffset</em>, </td>
4297 </tr>
4298 <tr>
4299 <td class="paramkey"></td>
4300 <td></td>
4301 <td class="paramtype">uint32_t&#160;</td>
4302 <td class="paramname"><em>padLeft</em> = <code>1</code>, </td>
4303 </tr>
4304 <tr>
4305 <td class="paramkey"></td>
4306 <td></td>
4307 <td class="paramtype">uint32_t&#160;</td>
4308 <td class="paramname"><em>padTop</em> = <code>1</code>, </td>
4309 </tr>
4310 <tr>
4311 <td class="paramkey"></td>
4312 <td></td>
4313 <td class="paramtype">uint32_t&#160;</td>
4314 <td class="paramname"><em>padRight</em> = <code>1</code>, </td>
4315 </tr>
4316 <tr>
4317 <td class="paramkey"></td>
4318 <td></td>
4319 <td class="paramtype">uint32_t&#160;</td>
4320 <td class="paramname"><em>padBottom</em> = <code>1</code>, </td>
4321 </tr>
4322 <tr>
4323 <td class="paramkey"></td>
4324 <td></td>
4325 <td class="paramtype">uint32_t&#160;</td>
4326 <td class="paramname"><em>strideX</em> = <code>1</code>, </td>
4327 </tr>
4328 <tr>
4329 <td class="paramkey"></td>
4330 <td></td>
4331 <td class="paramtype">uint32_t&#160;</td>
4332 <td class="paramname"><em>strideY</em> = <code>1</code>&#160;</td>
4333 </tr>
4334 <tr>
4335 <td></td>
4336 <td>)</td>
4337 <td></td><td></td>
4338 </tr>
4339 </table>
4340</div><div class="memdoc">
4341
4342<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00367">367</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4343
4344<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <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#l01159">IWorkloadFactory::CreateConvolution2d()</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#l00177">Convolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00438">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00440">Convolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00428">Convolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00422">Convolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00424">Convolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00426">Convolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00430">Convolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00432">Convolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00176">Convolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="_numeric_cast_8hpp_source.xhtml#l00033">armnn::numeric_cast()</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult&lt; T, n &gt;::output</a>, and <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>.</p>
4345<div class="fragment"><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160;{</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(qScale, qOffset);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[0]);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[3]);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[1]);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(input.shape()[2]);</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; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChanMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[0]);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[3]);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[1]);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(kernel.shape()[2]);</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; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[0]);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[3]);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[1]);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputExpected.shape()[2]);</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; <span class="keywordtype">bool</span> biasEnabled = bias.size() &gt; 0;</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="comment">// Creates the tensors.</span></div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, ArmnnType);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; ArmnnType);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(bias.size())}, ArmnnBType);</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; <span class="comment">// Construct the input data.</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; std::vector&lt;T&gt; inputData;</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; <span class="keyword">auto</span> batchedInput = MakeTensor&lt;T, 4&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; <span class="comment">// Construct the output data, with bias applied, as appropriate.</span></div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; std::vector&lt;T&gt; outputData;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, outputData);</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; std::unique_ptr&lt;armnn::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="l00422"></a><span class="lineno"> 422</span>&#160; std::unique_ptr&lt;armnn::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="l00423"></a><span class="lineno"> 423</span>&#160;</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</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="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</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; <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor; <span class="comment">// Still set this whether or not bias is enabled - can be a source of bugs.</span></div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</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; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</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; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;batchedInput[0][0][0][0]);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160;</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div>
4346<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div>
4347<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::Convolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00177">WorkloadData.hpp:177</a></div></div>
4348<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div>
4349<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>
4350<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div>
4351<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>
4352<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>
4353<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div>
4354<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div>
4355<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::Convolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00176">WorkloadData.hpp:176</a></div></div>
4356<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div>
4357<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
4358<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>
4359<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div>
4360<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>
4361<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div>
4362<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
4363<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
4364<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>
4365<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>
4366<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div>
4367<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2184995027cd2c9f9980206de9658855"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">armnn::IWorkloadFactory::CreateConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConvolution2d(const Convolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01159">WorkloadFactory.cpp:1159</a></div></div>
4368<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>
4369</div><!-- fragment -->
4370</div>
4371</div>
4372<a id="a7bd1547ceefdc1acedbb1fa6445b2968"></a>
4373<h2 class="memtitle"><span class="permalink"><a href="#a7bd1547ceefdc1acedbb1fa6445b2968">&#9670;&nbsp;</a></span>SimpleConvolution2dTestImpl()</h2>
4374
4375<div class="memitem">
4376<div class="memproto">
4377 <table class="memname">
4378 <tr>
4379 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleConvolution2dTestImpl </td>
4380 <td>(</td>
4381 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4382 <td class="paramname"><em>workloadFactory</em>, </td>
4383 </tr>
4384 <tr>
4385 <td class="paramkey"></td>
4386 <td></td>
4387 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4388 <td class="paramname"><em>memoryManager</em>, </td>
4389 </tr>
4390 <tr>
4391 <td class="paramkey"></td>
4392 <td></td>
4393 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
4394 <td class="paramname"><em>originalInput</em>, </td>
4395 </tr>
4396 <tr>
4397 <td class="paramkey"></td>
4398 <td></td>
4399 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
4400 <td class="paramname"><em>originalKernel</em>, </td>
4401 </tr>
4402 <tr>
4403 <td class="paramkey"></td>
4404 <td></td>
4405 <td class="paramtype">const boost::multi_array&lt; B, 1 &gt; &amp;&#160;</td>
4406 <td class="paramname"><em>bias</em>, </td>
4407 </tr>
4408 <tr>
4409 <td class="paramkey"></td>
4410 <td></td>
4411 <td class="paramtype">const boost::multi_array&lt; T, 4 &gt; &amp;&#160;</td>
4412 <td class="paramname"><em>originalOutputExpected</em>, </td>
4413 </tr>
4414 <tr>
4415 <td class="paramkey"></td>
4416 <td></td>
4417 <td class="paramtype">float&#160;</td>
4418 <td class="paramname"><em>qScale</em>, </td>
4419 </tr>
4420 <tr>
4421 <td class="paramkey"></td>
4422 <td></td>
4423 <td class="paramtype">int32_t&#160;</td>
4424 <td class="paramname"><em>qOffset</em>, </td>
4425 </tr>
4426 <tr>
4427 <td class="paramkey"></td>
4428 <td></td>
4429 <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&#160;</td>
4430 <td class="paramname"><em>layout</em> = <code><a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></code>, </td>
4431 </tr>
4432 <tr>
4433 <td class="paramkey"></td>
4434 <td></td>
4435 <td class="paramtype">uint32_t&#160;</td>
4436 <td class="paramname"><em>padLeft</em> = <code>0</code>, </td>
4437 </tr>
4438 <tr>
4439 <td class="paramkey"></td>
4440 <td></td>
4441 <td class="paramtype">uint32_t&#160;</td>
4442 <td class="paramname"><em>padTop</em> = <code>0</code>, </td>
4443 </tr>
4444 <tr>
4445 <td class="paramkey"></td>
4446 <td></td>
4447 <td class="paramtype">uint32_t&#160;</td>
4448 <td class="paramname"><em>padRight</em> = <code>0</code>, </td>
4449 </tr>
4450 <tr>
4451 <td class="paramkey"></td>
4452 <td></td>
4453 <td class="paramtype">uint32_t&#160;</td>
4454 <td class="paramname"><em>padBottom</em> = <code>0</code>, </td>
4455 </tr>
4456 <tr>
4457 <td class="paramkey"></td>
4458 <td></td>
4459 <td class="paramtype">uint32_t&#160;</td>
4460 <td class="paramname"><em>strideX</em> = <code>1</code>, </td>
4461 </tr>
4462 <tr>
4463 <td class="paramkey"></td>
4464 <td></td>
4465 <td class="paramtype">uint32_t&#160;</td>
4466 <td class="paramname"><em>strideY</em> = <code>1</code>, </td>
4467 </tr>
4468 <tr>
4469 <td class="paramkey"></td>
4470 <td></td>
4471 <td class="paramtype">uint32_t&#160;</td>
4472 <td class="paramname"><em>dilationX</em> = <code>1</code>, </td>
4473 </tr>
4474 <tr>
4475 <td class="paramkey"></td>
4476 <td></td>
4477 <td class="paramtype">uint32_t&#160;</td>
4478 <td class="paramname"><em>dilationY</em> = <code>1</code>&#160;</td>
4479 </tr>
4480 <tr>
4481 <td></td>
4482 <td>)</td>
4483 <td></td><td></td>
4484 </tr>
4485 </table>
4486</div><div class="memdoc">
4487
4488<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00201">201</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4489
4490<p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l00169">ApplyBias()</a>, <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#l01159">IWorkloadFactory::CreateConvolution2d()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00264">TensorInfo::GetQuantizationOffset()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00247">TensorInfo::GetQuantizationScale()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00088">TensorInfo::GetShape()</a>, <a class="el" href="_tensor_utils_8cpp_source.xhtml#l00038">armnnUtils::GetTensorInfo()</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#l00177">Convolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00438">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00440">Convolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00434">Convolution2dDescriptor::m_DilationX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00436">Convolution2dDescriptor::m_DilationY</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00428">Convolution2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00422">Convolution2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00424">Convolution2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00426">Convolution2dDescriptor::m_PadTop</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00430">Convolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00432">Convolution2dDescriptor::m_StrideY</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00176">Convolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, <a class="el" href="_numeric_cast_8hpp_source.xhtml#l00033">armnn::numeric_cast()</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="_permute_8cpp_source.xhtml#l00121">armnnUtils::Permute()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00275">TensorInfo::SetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00259">TensorInfo::SetQuantizationScale()</a>.</p>
4491<div class="fragment"><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160;{</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[2]);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[3]);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[1]);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalInput.shape()[0]);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[2]);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[3]);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[1]);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalOutputExpected.shape()[0]);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[2]);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[3]);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[1]);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelDepthMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(originalKernel.shape()[0]);</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160;</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; <span class="keywordtype">bool</span> biasEnabled = bias.size() &gt; 0;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; <span class="comment">// This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; BOOST_ASSERT(inputNum == 1);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; BOOST_ASSERT(outputNum == 1);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; <span class="comment">// If a bias is used, its size must equal the number of output channels.</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; <span class="comment">// Note these tensors will use two (identical) batches.</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc =</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout, ArmnnType);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(bias.size())}, ArmnnBType);</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; <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; {</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; inputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; kernelDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; kernelDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; biasDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; <span class="comment">// Construct input data - two batches of the same input image.</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; std::vector&lt;T&gt; inputImage;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; inputImage.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; std::vector&lt;T&gt; inputData;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; <span class="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; {</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; std::vector&lt;T&gt; tmp(inputData.size());</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; inputData = tmp;</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;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; <span class="keyword">auto</span> batchedInput = MakeTensor&lt;T, 4&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; std::vector&lt;T&gt; outputImage;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; outputImage.assign(originalOutputExpected.data(),</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; <span class="comment">// Apply bias to output image if it is enabled.</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; {</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; std::vector&lt;T&gt; biasV;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; biasV.assign(bias.data(), bias.data() + outputChannels);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputImage, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; outputWidth, outputHeight);</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;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; <span class="comment">// Construct expected output data - two identical images.</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; std::vector&lt;T&gt; outputData;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());</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">// at this point if we require it permute the expected output</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; std::vector&lt;T&gt; tmp(outputData.size());</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; outputData = tmp;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; }</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo, outputData);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; std::unique_ptr&lt;armnn::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="l00317"></a><span class="lineno"> 317</span>&#160; std::unique_ptr&lt;armnn::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="l00318"></a><span class="lineno"> 318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; <span class="comment">// Permute the kernel if necessary</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; boost::multi_array&lt;T, 4&gt; kernel = boost::multi_array&lt;T, 4&gt;(originalKernel);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; {</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(kernelDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, originalKernel.data(), kernel.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; }</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;weightsTensor, &amp;kernel[0][0][0][0]);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; {</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;biasTensor, &amp;bias[0]);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightsTensor;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor; <span class="comment">// Still set this whether or not bias is enabled - can be a source of bugs.</span></div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = dilationX;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = dilationY;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; outputHandle-&gt;Allocate();</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; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;batchedInput[0][0][0][0]);</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; ExecuteWorkload(*workload, memoryManager);</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; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</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; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div>
4492<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div>
4493<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::Convolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00177">WorkloadData.hpp:177</a></div></div>
4494<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div>
4495<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>
4496<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa1f4ce02e0904dc8cf1b7f42bc34d346"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a></div><div class="ttdeci">void ApplyBias(std::vector&lt; T &gt; &amp;v, float vScale, int32_t vOffset, const std::vector&lt; B &gt; &amp;bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00169">Conv2dTestImpl.cpp:169</a></div></div>
4497<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>
4498<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div>
4499<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>
4500<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00436">Descriptors.hpp:436</a></div></div>
4501<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>
4502<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div>
4503<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div>
4504<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div>
4505<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div>
4506<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</a></div></div>
4507<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::Convolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00176">WorkloadData.hpp:176</a></div></div>
4508<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>
4509<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div>
4510<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
4511<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>
4512<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>
4513<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div>
4514<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>
4515<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div>
4516<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00434">Descriptors.hpp:434</a></div></div>
4517<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
4518<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
4519<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div>
4520<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>
4521<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>
4522<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div>
4523<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div>
4524<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
4525<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2184995027cd2c9f9980206de9658855"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">armnn::IWorkloadFactory::CreateConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateConvolution2d(const Convolution2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01159">WorkloadFactory.cpp:1159</a></div></div>
4526<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>
4527</div><!-- fragment -->
4528</div>
4529</div>
4530<a id="a77a29527216d36bce78e88354462ede8"></a>
4531<h2 class="memtitle"><span class="permalink"><a href="#a77a29527216d36bce78e88354462ede8">&#9670;&nbsp;</a></span>SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTest()</h2>
4532
4533<div class="memitem">
4534<div class="memproto">
4535 <table class="memname">
4536 <tr>
4537 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;float, 4&gt; SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTest </td>
4538 <td>(</td>
4539 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4540 <td class="paramname"><em>workloadFactory</em>, </td>
4541 </tr>
4542 <tr>
4543 <td class="paramkey"></td>
4544 <td></td>
4545 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4546 <td class="paramname"><em>memoryManager</em>&#160;</td>
4547 </tr>
4548 <tr>
4549 <td></td>
4550 <td>)</td>
4551 <td></td><td></td>
4552 </tr>
4553 </table>
4554</div><div class="memdoc">
4555
4556<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l03284">3284</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4557<div class="fragment"><div class="line"><a name="l03287"></a><span class="lineno"> 3287</span>&#160;{</div><div class="line"><a name="l03288"></a><span class="lineno"> 3288</span>&#160; <span class="keywordflow">return</span> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon&lt;armnn::DataType::Float32, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l03289"></a><span class="lineno"> 3289</span>&#160; workloadFactory,</div><div class="line"><a name="l03290"></a><span class="lineno"> 3290</span>&#160; memoryManager,</div><div class="line"><a name="l03291"></a><span class="lineno"> 3291</span>&#160; 0.f,</div><div class="line"><a name="l03292"></a><span class="lineno"> 3292</span>&#160; 0,</div><div class="line"><a name="l03293"></a><span class="lineno"> 3293</span>&#160; <span class="keyword">false</span>);</div><div class="line"><a name="l03294"></a><span class="lineno"> 3294</span>&#160;}</div></div><!-- fragment -->
4558</div>
4559</div>
4560<a id="ac7af28eafb5b583057bca4471ce22328"></a>
4561<h2 class="memtitle"><span class="permalink"><a href="#ac7af28eafb5b583057bca4471ce22328">&#9670;&nbsp;</a></span>SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon()</h2>
4562
4563<div class="memitem">
4564<div class="memproto">
4565 <table class="memname">
4566 <tr>
4567 <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>&lt;T, 4&gt; SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon </td>
4568 <td>(</td>
4569 <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &amp;&#160;</td>
4570 <td class="paramname"><em>workloadFactory</em>, </td>
4571 </tr>
4572 <tr>
4573 <td class="paramkey"></td>
4574 <td></td>
4575 <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
4576 <td class="paramname"><em>memoryManager</em>, </td>
4577 </tr>
4578 <tr>
4579 <td class="paramkey"></td>
4580 <td></td>
4581 <td class="paramtype">float&#160;</td>
4582 <td class="paramname"><em>qScale</em>, </td>
4583 </tr>
4584 <tr>
4585 <td class="paramkey"></td>
4586 <td></td>
4587 <td class="paramtype">int32_t&#160;</td>
4588 <td class="paramname"><em>qOffset</em>, </td>
4589 </tr>
4590 <tr>
4591 <td class="paramkey"></td>
4592 <td></td>
4593 <td class="paramtype">bool&#160;</td>
4594 <td class="paramname"><em>biasEnabled</em>&#160;</td>
4595 </tr>
4596 <tr>
4597 <td></td>
4598 <td>)</td>
4599 <td></td><td></td>
4600 </tr>
4601 </table>
4602</div><div class="memdoc">
4603
4604<p class="definition">Definition at line <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml#l02212">2212</a> of file <a class="el" href="_conv2d_test_impl_8cpp_source.xhtml">Conv2dTestImpl.cpp</a>.</p>
4605
4606<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p>
4607<div class="fragment"><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>&#160;{</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>&#160; <span class="keyword">auto</span> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>&#160;</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 9, 9}, ArmnnType);</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>&#160; 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>&#160; 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>&#160; 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>&#160; 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>&#160; },</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>&#160; inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>&#160; inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>&#160;</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>&#160; <span class="keyword">auto</span> kernel = MakeTensor&lt;T, 4&gt;(kernelTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>&#160; 1, 2, 3,</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>&#160; 4, 5, 6,</div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>&#160; 7, 8, 9</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>&#160; },</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>&#160; kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>&#160; kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>&#160;</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>&#160; uint32_t padLeft = 0;</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>&#160; uint32_t padTop = 0;</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>&#160; uint32_t padRight = 0;</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>&#160; uint32_t padBottom = 0;</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>&#160; uint32_t strideX = 1;</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>&#160; uint32_t strideY = 1;</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>&#160; uint32_t dilationX = 3;</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>&#160; uint32_t dilationY = 3;</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>&#160;</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>&#160; <span class="comment">// Since the dilation rate is 3 this will reduce the size of the output from 9x9 to 3x3 of all 5s.</span></div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>&#160; boost::multi_array&lt;T, 4&gt; expectedOutput = MakeTensor&lt;T, 4&gt;(outputTensorInfo, std::vector&lt;T&gt;(</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>&#160; QuantizedVector&lt;T&gt;({</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>&#160; 5, 5, 5,</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>&#160; 5, 5, 5,</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>&#160; 5, 5, 5</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>&#160; },</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>&#160; outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>&#160; outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>&#160;</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>&#160; <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl&lt;ArmnnType, ArmnnBType&gt;(</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>&#160; workloadFactory,</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>&#160; memoryManager,</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>&#160; input,</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>&#160; kernel,</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>&#160; GetBias2&lt;ArmnnBType&gt;(biasEnabled, qScale * qScale),</div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>&#160; expectedOutput,</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>&#160; qScale,</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>&#160; qOffset,</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>&#160; layout,</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>&#160; padLeft,</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>&#160; padTop,</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>&#160; padRight,</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>&#160; padBottom,</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span>&#160; strideX,</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>&#160; strideY,</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>&#160; dilationX,</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>&#160; dilationY);</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</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>
4608<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
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4617 <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="_conv2d_test_impl_8cpp.xhtml">Conv2dTestImpl.cpp</a></li>
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