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| author | Nikhil Raj <nikhil.raj@arm.com> | 2022-05-24 11:32:07 +0100 |
|---|---|---|
| committer | Nikhil Raj <nikhil.raj@arm.com> | 2022-05-24 11:32:07 +0100 |
| commit | 549b9600a6eaf0727fa084465a75f173edf8f381 (patch) | |
| tree | 9c9b054417504444fff067b74eaa1811b74e6d06 /22.05/namespacearmnn_1_1experimental.xhtml | |
| parent | f4019872c1134c6fcc1d6993e5746f55c1e79208 (diff) | |
| download | armnn-549b9600a6eaf0727fa084465a75f173edf8f381.tar.gz | |
Update 22.05 Doxygen Docs after updates to main Readme
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I56711772406a41ff81fa136a5fb6c59c9b9cf504
Diffstat (limited to '22.05/namespacearmnn_1_1experimental.xhtml')
| -rw-r--r-- | 22.05/namespacearmnn_1_1experimental.xhtml | 497 |
1 files changed, 497 insertions, 0 deletions
diff --git a/22.05/namespacearmnn_1_1experimental.xhtml b/22.05/namespacearmnn_1_1experimental.xhtml new file mode 100644 index 0000000000..f9e1a7baeb --- /dev/null +++ b/22.05/namespacearmnn_1_1experimental.xhtml @@ -0,0 +1,497 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: armnn::experimental Namespace Reference</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" 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href="#func-members">Functions</a> </div> + <div class="headertitle"> +<div class="title">armnn::experimental Namespace Reference</div> </div> +</div><!--header--> +<div class="contents"> +<table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a> +Classes</h2></td></tr> +<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1experimental_1_1_async_callback_manager.xhtml">AsyncCallbackManager</a></td></tr> +<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1experimental_1_1_async_execution_callback.xhtml">AsyncExecutionCallback</a></td></tr> +<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1experimental_1_1_i_async_execution_callback.xhtml">IAsyncExecutionCallback</a></td></tr> +<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1experimental_1_1_i_working_mem_handle.xhtml">IWorkingMemHandle</a></td></tr> +<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1experimental_1_1_threadpool.xhtml">Threadpool</a></td></tr> +<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a></td></tr> +<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml">WorkingMemHandle</a></td></tr> +<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> +</table><table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a> +Typedefs</h2></td></tr> +<tr class="memitem:ab3dbd9e80d760b3c2c1eff87ca226d12"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1experimental.xhtml#ab3dbd9e80d760b3c2c1eff87ca226d12">IAsyncExecutionCallbackPtr</a> = std::shared_ptr< <a class="el" href="classarmnn_1_1experimental_1_1_i_async_execution_callback.xhtml">IAsyncExecutionCallback</a> ></td></tr> +<tr class="separator:ab3dbd9e80d760b3c2c1eff87ca226d12"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:aeeb110875a2be4ca0c3595aaad6397fc"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1experimental.xhtml#aeeb110875a2be4ca0c3595aaad6397fc">InferenceId</a> = uint64_t</td></tr> +<tr class="separator:aeeb110875a2be4ca0c3595aaad6397fc"><td class="memSeparator" colspan="2"> </td></tr> +</table><table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a> +Functions</h2></td></tr> +<tr class="memitem:ae9bd946ed0ec9f8a41197b83037a401f"><td class="memTemplParams" colspan="2">template<DataType ArmnnIType, DataType ArmnnOType, typename TInput = ResolveType <ArmnnIType>, typename TOutput = ResolveType <ArmnnOType>> </td></tr> +<tr class="memitem:ae9bd946ed0ec9f8a41197b83037a401f"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1experimental.xhtml#ae9bd946ed0ec9f8a41197b83037a401f">AsyncThreadedEndToEndTestImpl</a> (<a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network, const std::vector< std::map< int, std::vector< TInput >>> &inputTensorData, const std::vector< std::map< int, std::vector< TOutput >>> &expectedOutputData, std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > backends, const size_t numberOfInferences, float tolerance=0.000001f)</td></tr> +<tr class="separator:ae9bd946ed0ec9f8a41197b83037a401f"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a6c8cd7552424617a2e4361c1d966f734"><td class="memTemplParams" colspan="2">template<DataType ArmnnIType, DataType ArmnnOType, typename TInput = ResolveType<ArmnnIType>, typename TOutput = ResolveType<ArmnnOType>> </td></tr> +<tr class="memitem:a6c8cd7552424617a2e4361c1d966f734"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1experimental.xhtml#a6c8cd7552424617a2e4361c1d966f734">AsyncEndToEndTestImpl</a> (<a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network, const std::map< int, std::vector< TInput >> &inputTensorData, const std::map< int, std::vector< TOutput >> &expectedOutputData, std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > backends, float tolerance=0.000001f, size_t numThreads=1)</td></tr> +<tr class="separator:a6c8cd7552424617a2e4361c1d966f734"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:adef78ee82cfcce4c9061f266ccf0a29d"><td class="memTemplParams" colspan="2">template<typename armnn::DataType DataType> </td></tr> +<tr class="memitem:adef78ee82cfcce4c9061f266ccf0a29d"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1experimental.xhtml#adef78ee82cfcce4c9061f266ccf0a29d">CreateStridedSliceNetwork</a> (const <a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> &inputShape, const <a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> &outputShape, const std::vector< int > &beginData, const std::vector< int > &endData, const std::vector< int > &stridesData, int beginMask=0, int endMask=0, int shrinkAxisMask=0, int ellipsisMask=0, int newAxisMask=0, const float qScale=1.0f, const int32_t qOffset=0)</td></tr> +<tr class="separator:adef78ee82cfcce4c9061f266ccf0a29d"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a88ec5950dc1ba35b8932373b5eda2729"><td class="memTemplParams" colspan="2">template<armnn::DataType ArmnnType> </td></tr> +<tr class="memitem:a88ec5950dc1ba35b8932373b5eda2729"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1experimental.xhtml#a88ec5950dc1ba35b8932373b5eda2729">StridedSlicedEndToEndTest</a> (const std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > &backends, size_t numThreads)</td></tr> +<tr class="separator:a88ec5950dc1ba35b8932373b5eda2729"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:abc5302ddb43cf5de9a847fce043bae9b"><td class="memTemplParams" colspan="2">template<armnn::DataType ArmnnType> </td></tr> +<tr class="memitem:abc5302ddb43cf5de9a847fce043bae9b"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1experimental.xhtml#abc5302ddb43cf5de9a847fce043bae9b">StridedSlicedMultiThreadedEndToEndTest</a> (const std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > &backends)</td></tr> +<tr class="separator:abc5302ddb43cf5de9a847fce043bae9b"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<h2 class="groupheader">Typedef Documentation</h2> +<a id="ab3dbd9e80d760b3c2c1eff87ca226d12"></a> +<h2 class="memtitle"><span class="permalink"><a href="#ab3dbd9e80d760b3c2c1eff87ca226d12">◆ </a></span>IAsyncExecutionCallbackPtr</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">using <a class="el" href="namespacearmnn_1_1experimental.xhtml#ab3dbd9e80d760b3c2c1eff87ca226d12">IAsyncExecutionCallbackPtr</a> = std::shared_ptr<<a class="el" href="classarmnn_1_1experimental_1_1_i_async_execution_callback.xhtml">IAsyncExecutionCallback</a>></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_i_async_execution_callback_8hpp_source.xhtml#l00017">17</a> of file <a class="el" href="_i_async_execution_callback_8hpp_source.xhtml">IAsyncExecutionCallback.hpp</a>.</p> + +</div> +</div> +<a id="aeeb110875a2be4ca0c3595aaad6397fc"></a> +<h2 class="memtitle"><span class="permalink"><a href="#aeeb110875a2be4ca0c3595aaad6397fc">◆ </a></span>InferenceId</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">using <a class="el" href="namespacearmnn_1_1experimental.xhtml#aeeb110875a2be4ca0c3595aaad6397fc">InferenceId</a> = uint64_t</td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_async_execution_callback_8hpp_source.xhtml#l00024">24</a> of file <a class="el" href="_async_execution_callback_8hpp_source.xhtml">AsyncExecutionCallback.hpp</a>.</p> + +</div> +</div> +<h2 class="groupheader">Function Documentation</h2> +<a id="a6c8cd7552424617a2e4361c1d966f734"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a6c8cd7552424617a2e4361c1d966f734">◆ </a></span>AsyncEndToEndTestImpl()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">void armnn::experimental::AsyncEndToEndTestImpl </td> + <td>(</td> + <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> </td> + <td class="paramname"><em>network</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::map< int, std::vector< TInput >> & </td> + <td class="paramname"><em>inputTensorData</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::map< int, std::vector< TOutput >> & </td> + <td class="paramname"><em>expectedOutputData</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > </td> + <td class="paramname"><em>backends</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">float </td> + <td class="paramname"><em>tolerance</em> = <code>0.000001f</code>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">size_t </td> + <td class="paramname"><em>numThreads</em> = <code>1</code> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml#l00123">123</a> of file <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml">StridedSliceAsyncEndToEndTest.hpp</a>.</p> + +<p class="reference">References <a class="el" href="_assert_8hpp_source.xhtml#l00014">ARMNN_ASSERT</a>, <a class="el" href="_runtime_8cpp_source.xhtml#l00049">IRuntime::Create()</a>, <a class="el" href="_async_execution_callback_8cpp_source.xhtml#l00060">AsyncCallbackManager::GetNotifiedCallback()</a>, <a class="el" href="_network_8cpp_source.xhtml#l01847">armnn::Optimize()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00514">TensorInfo::SetConstant()</a>, <a class="el" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">armnn::Success</a>, and <a class="el" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Undefined</a>.</p> +<div class="fragment"><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numThreads >= 1);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfInferences = numThreads == 1 ? 1 : 1000;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="comment">// Create Runtime in which test will run</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(IRuntime::Create(options));</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Optimize the Network</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// Creates AsyncNetwork</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkId = 0;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  std::string errorMessage;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, MemorySource::Undefined, MemorySource::Undefined);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  runtime->LoadNetwork(networkId, std::move(optNet), errorMessage, networkProperties);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  inputTensors.reserve(inputTensorData.size());</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : inputTensorData)</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = runtime->GetInputTensorInfo(networkId, it.first);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  inputTensors.push_back({it.first,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(inputTensorInfo, it.second.data())});</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  }</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  std::vector<OutputTensors> outputTensorsVec;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  std::vector<std::map<int, std::vector<TOutput>>> outputStorageVec;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  outputTensorsVec.reserve(numberOfInferences);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  outputStorageVec.reserve(numberOfInferences);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numberOfInferences; ++i)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  {</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  outputStorageVec.emplace_back(std::map<<span class="keywordtype">int</span>, std::vector<TOutput>>());</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  outputTensors.reserve(expectedOutputData.size());</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  std::vector<TOutput> out(it.second.size());</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  outputStorageVec[i].emplace(it.first, out);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  outputTensors.push_back({it.first,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runtime->GetOutputTensorInfo(networkId, it.first),</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  outputStorageVec[i].at(it.first).data())});</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  outputTensorsVec.push_back(outputTensors);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  }</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">if</span> (numThreads == 1)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="comment">// Create WorkingMemHandle for this async network</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  std::unique_ptr<IWorkingMemHandle> workingMemHandle = runtime->CreateWorkingMemHandle(networkId);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get();</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="comment">// Run the async network</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  runtime->Execute(workingMemHandleRef, inputTensors, outputTensorsVec[0]);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  {</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  std::vector<std::shared_ptr<IWorkingMemHandle>> memHandles;</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < numThreads; ++i)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  memHandles.emplace_back(runtime->CreateWorkingMemHandle(networkId));</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  Threadpool threadpool(numThreads, runtime.get(), memHandles);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  AsyncCallbackManager callbackManager;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="comment">// For the asyncronous execution, we are adding a pool of working memory handles (1 per thread) in the</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="comment">// LoadedNetwork with each scheduled inference having a random priority</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < numberOfInferences; ++i)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  threadpool.Schedule(networkId,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  inputTensors,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  outputTensorsVec[i],</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  static_cast<QosExecPriority>(rand()%3),</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  callbackManager.GetNewCallback());</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Wait until the execution signals a notify</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < numberOfInferences; ++i)</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  {</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">auto</span> cb = callbackManager.GetNotifiedCallback();</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// Checks the results.</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  CHECK(cb->GetStatus() == Status::Success);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  }</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& outputStorage : outputStorageVec)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  {</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  std::vector<TOutput> out = outputStorage.at(it.first);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < out.size(); ++i)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="comment">//CHECK(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == true);</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  CHECK(it.second[i] == doctest::Approx(out[i]).epsilon(tolerance));</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  }</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> }</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#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00033">IRuntime.hpp:33</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00392">Tensor.hpp:392</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_network_properties_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_network_properties.xhtml">armnn::INetworkProperties</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00035">IRuntime.hpp:35</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00319">Tensor.hpp:319</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01847">Network.cpp:1847</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a0d8160388a127c1a23b37bc88dc6e2ec"><div class="ttname"><a href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00027">IRuntime.hpp:27</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00393">Tensor.hpp:393</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00242">INetwork.hpp:242</a></div></div> +<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00077">IRuntime.hpp:77</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00514">Tensor.cpp:514</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="ae9bd946ed0ec9f8a41197b83037a401f"></a> +<h2 class="memtitle"><span class="permalink"><a href="#ae9bd946ed0ec9f8a41197b83037a401f">◆ </a></span>AsyncThreadedEndToEndTestImpl()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">void armnn::experimental::AsyncThreadedEndToEndTestImpl </td> + <td>(</td> + <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> </td> + <td class="paramname"><em>network</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::vector< std::map< int, std::vector< TInput >>> & </td> + <td class="paramname"><em>inputTensorData</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::vector< std::map< int, std::vector< TOutput >>> & </td> + <td class="paramname"><em>expectedOutputData</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > </td> + <td class="paramname"><em>backends</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const size_t </td> + <td class="paramname"><em>numberOfInferences</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">float </td> + <td class="paramname"><em>tolerance</em> = <code>0.000001f</code> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml#l00030">30</a> of file <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml">StridedSliceAsyncEndToEndTest.hpp</a>.</p> + +<p class="reference">References <a class="el" href="_runtime_8cpp_source.xhtml#l00049">IRuntime::Create()</a>, <a class="el" href="_network_8cpp_source.xhtml#l01847">armnn::Optimize()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00514">TensorInfo::SetConstant()</a>, and <a class="el" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Undefined</a>.</p> +<div class="fragment"><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="comment">// Create Runtime in which test will run</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(IRuntime::Create(options));</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="comment">// Optimize the Network</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Creates AsyncNetwork</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkId = 0;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::string errorMessage;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, MemorySource::Undefined, MemorySource::Undefined);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  runtime->LoadNetwork(networkId, std::move(optNet), errorMessage, networkProperties);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  std::vector<InputTensors> inputTensorsVec;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  std::vector<OutputTensors> outputTensorsVec;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  std::vector<std::map<int, std::vector<TOutput>>> outputStorageVec;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  std::vector<std::unique_ptr<IWorkingMemHandle>> workingMemHandles;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numberOfInferences; ++i)</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  {</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  outputStorageVec.emplace_back(std::map<<span class="keywordtype">int</span>, std::vector<TOutput>>());</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  inputTensors.reserve(inputTensorData.size());</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : inputTensorData[i])</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = runtime->GetInputTensorInfo(networkId, it.first);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  inputTensors.push_back({it.first,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(inputTensorInfo, it.second.data())});</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  }</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  outputTensors.reserve(expectedOutputData.size());</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData[i])</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  std::vector<TOutput> out(it.second.size());</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  outputStorageVec[i].emplace(it.first, out);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  outputTensors.push_back({it.first,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runtime->GetOutputTensorInfo(networkId, it.first),</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  outputStorageVec[i].at(it.first).data())});</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  inputTensorsVec.push_back(inputTensors);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  outputTensorsVec.push_back(outputTensors);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  workingMemHandles.push_back(runtime->CreateWorkingMemHandle(networkId));</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  }</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::vector<std::thread> threads;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numberOfInferences; ++i)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">// Access the vectors before we do anything multi-threaded</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a>& inputTensors = inputTensorsVec[i];</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>& outputTensors = outputTensorsVec[i];</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  IWorkingMemHandle& workingMemHandle = *workingMemHandles[i].get();</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  threads.emplace_back([&]()</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// Run the async network</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  runtime->Execute(workingMemHandle, inputTensors, outputTensors);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  });</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numberOfInferences; ++i)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  threads[i].join();</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// Checks the results.</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numberOfInferences; ++i)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  {</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> &&it : expectedOutputData[i])</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::vector<TOutput> out = outputStorageVec[i].at(it.first);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < out.size(); ++j)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  CHECK(Compare<ArmnnOType>(it.second[j], out[j], tolerance) == <span class="keyword">true</span>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> }</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#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00033">IRuntime.hpp:33</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00392">Tensor.hpp:392</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_network_properties_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_network_properties.xhtml">armnn::INetworkProperties</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00035">IRuntime.hpp:35</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00319">Tensor.hpp:319</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01847">Network.cpp:1847</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a0d8160388a127c1a23b37bc88dc6e2ec"><div class="ttname"><a href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00027">IRuntime.hpp:27</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00393">Tensor.hpp:393</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00242">INetwork.hpp:242</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00077">IRuntime.hpp:77</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00514">Tensor.cpp:514</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="adef78ee82cfcce4c9061f266ccf0a29d"></a> +<h2 class="memtitle"><span class="permalink"><a href="#adef78ee82cfcce4c9061f266ccf0a29d">◆ </a></span>CreateStridedSliceNetwork()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> armnn::experimental::CreateStridedSliceNetwork </td> + <td>(</td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> & </td> + <td class="paramname"><em>inputShape</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> & </td> + <td class="paramname"><em>outputShape</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::vector< int > & </td> + <td class="paramname"><em>beginData</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::vector< int > & </td> + <td class="paramname"><em>endData</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::vector< int > & </td> + <td class="paramname"><em>stridesData</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">int </td> + <td class="paramname"><em>beginMask</em> = <code>0</code>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">int </td> + <td class="paramname"><em>endMask</em> = <code>0</code>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">int </td> + <td class="paramname"><em>shrinkAxisMask</em> = <code>0</code>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">int </td> + <td class="paramname"><em>ellipsisMask</em> = <code>0</code>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">int </td> + <td class="paramname"><em>newAxisMask</em> = <code>0</code>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const float </td> + <td class="paramname"><em>qScale</em> = <code>1.0f</code>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const int32_t </td> + <td class="paramname"><em>qOffset</em> = <code>0</code> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml#l00240">240</a> of file <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml">StridedSliceAsyncEndToEndTest.hpp</a>.</p> + +<p class="reference">References <a class="el" href="_test_utils_8cpp_source.xhtml#l00014">Connect()</a>, <a class="el" href="_network_8cpp_source.xhtml#l00476">INetwork::Create()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01323">StridedSliceDescriptor::m_Begin</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01331">StridedSliceDescriptor::m_BeginMask</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01338">StridedSliceDescriptor::m_EllipsisMask</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01325">StridedSliceDescriptor::m_End</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01334">StridedSliceDescriptor::m_EndMask</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01341">StridedSliceDescriptor::m_NewAxisMask</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01336">StridedSliceDescriptor::m_ShrinkAxisMask</a>, and <a class="el" href="_descriptors_8hpp_source.xhtml#l01327">StridedSliceDescriptor::m_Stride</a>.</p> +<div class="fragment"><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> {</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a> stridedSliceDescriptor;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">m_Begin</a> = beginData;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">m_End</a> = endData;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">m_Stride</a> = stridesData;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">m_BeginMask</a> = beginMask;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = endMask;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">m_ShrinkAxisMask</a> = shrinkAxisMask;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">m_EllipsisMask</a> = ellipsisMask;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  stridedSliceDescriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">m_NewAxisMask</a> = newAxisMask;</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0, <span class="stringliteral">"Input_Layer"</span>);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* stridedSlice = net->AddStridedSliceLayer(stridedSliceDescriptor, <span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, stridedSlice, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(stridedSlice, output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordflow">return</span> net;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> }</div><div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00066">INetwork.hpp:66</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a6d0384878432cfc9652b7ae8bc59506f"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">armnn::StridedSliceDescriptor::m_ShrinkAxisMask</a></div><div class="ttdeci">int32_t m_ShrinkAxisMask</div><div class="ttdoc">Shrink axis mask value. If set, the nth specification shrinks the dimensionality by 1...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01336">Descriptors.hpp:1336</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a118fe06b7c2599da60398ee311ede923"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">armnn::StridedSliceDescriptor::m_Begin</a></div><div class="ttdeci">std::vector< int > m_Begin</div><div class="ttdoc">Begin values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01323">Descriptors.hpp:1323</a></div></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#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a61081be1483984e33db452c75d569f51"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">armnn::StridedSliceDescriptor::m_BeginMask</a></div><div class="ttdeci">int32_t m_BeginMask</div><div class="ttdoc">Begin mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01331">Descriptors.hpp:1331</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_ac37e49c0d6e6e54f9d2015d0f11f8ee7"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">armnn::StridedSliceDescriptor::m_EndMask</a></div><div class="ttdeci">int32_t m_EndMask</div><div class="ttdoc">End mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01334">Descriptors.hpp:1334</a></div></div> +<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#l00048">Types.hpp:48</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a7c91eda2b331d607bae92cd8ebf50bb9"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">armnn::StridedSliceDescriptor::m_NewAxisMask</a></div><div class="ttdeci">int32_t m_NewAxisMask</div><div class="ttdoc">New axis mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01341">Descriptors.hpp:1341</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_af996d82c47e43a16f4c8faa6c6b3e030"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">armnn::StridedSliceDescriptor::m_EllipsisMask</a></div><div class="ttdeci">int32_t m_EllipsisMask</div><div class="ttdoc">Ellipsis mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01338">Descriptors.hpp:1338</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a0d53caff836b84204adbd1c28752a201"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">armnn::StridedSliceDescriptor::m_Stride</a></div><div class="ttdeci">std::vector< int > m_Stride</div><div class="ttdoc">Stride values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01327">Descriptors.hpp:1327</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_aa68194dd6258ab5b04123005a066ea25"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">armnn::StridedSliceDescriptor::m_End</a></div><div class="ttdeci">std::vector< int > m_End</div><div class="ttdoc">End values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01325">Descriptors.hpp:1325</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01284">Descriptors.hpp:1284</a></div></div> +<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00014">TestUtils.cpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00241">INetwork.hpp:241</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00476">Network.cpp:476</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="a88ec5950dc1ba35b8932373b5eda2729"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a88ec5950dc1ba35b8932373b5eda2729">◆ </a></span>StridedSlicedEndToEndTest()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">void armnn::experimental::StridedSlicedEndToEndTest </td> + <td>(</td> + <td class="paramtype">const std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > & </td> + <td class="paramname"><em>backends</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">size_t </td> + <td class="paramname"><em>numThreads</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml#l00281">281</a> of file <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml">StridedSliceAsyncEndToEndTest.hpp</a>.</p> +<div class="fragment"><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> {</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = {3, 2, 3, 1};</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = {1, 2, 3, 1};</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keyword">const</span> std::vector<int>& beginData = {1, 0, 0, 0};</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keyword">const</span> std::vector<int>& endData = {2, 2, 3, 1};</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">const</span> std::vector<int>& stridesData = {1, 1, 1, 1};</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordtype">int</span> beginMask = 0;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordtype">int</span> endMask = 0;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordtype">int</span> shrinkAxisMask = 0;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keywordtype">int</span> ellipsisMask = 0;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordtype">int</span> newAxisMask = 0;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateStridedSliceNetwork<ArmnnType>(inputShape,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  outputShape,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  beginData,</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  endData,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  stridesData,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  beginMask,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  endMask,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  shrinkAxisMask,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  ellipsisMask,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  newAxisMask);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  CHECK(net);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  std::vector<T> inputData{</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  };</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  std::vector<T> outputExpected{</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  };</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  std::map<int, std::vector<T>> inputTensorData = {{0, inputData}};</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}};</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net),</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  inputTensorData,</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  expectedOutputData,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  backends,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  0.000001f,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  numThreads);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl< DT >::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00079">ResolveType.hpp:79</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00241">INetwork.hpp:241</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="abc5302ddb43cf5de9a847fce043bae9b"></a> +<h2 class="memtitle"><span class="permalink"><a href="#abc5302ddb43cf5de9a847fce043bae9b">◆ </a></span>StridedSlicedMultiThreadedEndToEndTest()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">void armnn::experimental::StridedSlicedMultiThreadedEndToEndTest </td> + <td>(</td> + <td class="paramtype">const std::vector< <a class="el" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> > & </td> + <td class="paramname"><em>backends</em></td><td>)</td> + <td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml#l00335">335</a> of file <a class="el" href="_strided_slice_async_end_to_end_test_8hpp_source.xhtml">StridedSliceAsyncEndToEndTest.hpp</a>.</p> +<div class="fragment"><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> {</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = {3, 2, 3, 1};</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = {1, 2, 3, 1};</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> std::vector<int>& beginData = {1, 0, 0, 0};</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keyword">const</span> std::vector<int>& endData = {2, 2, 3, 1};</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keyword">const</span> std::vector<int>& stridesData = {1, 1, 1, 1};</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordtype">int</span> beginMask = 0;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordtype">int</span> endMask = 0;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordtype">int</span> shrinkAxisMask = 0;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordtype">int</span> ellipsisMask = 0;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordtype">int</span> newAxisMask = 0;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateStridedSliceNetwork<ArmnnType>(inputShape,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  outputShape,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  beginData,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  endData,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  stridesData,</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  beginMask,</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  endMask,</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  shrinkAxisMask,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  ellipsisMask,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  newAxisMask);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> </div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  CHECK(net);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  std::vector<T> inputData1{</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  };</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  std::vector<T> outputExpected1{ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f };</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  std::vector<T> inputData2{</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  8.0f, 8.0f, 8.0f, 7.0f, 7.0f, 7.0f,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  };</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  std::vector<T> outputExpected2{ 8.0f, 8.0f, 8.0f, 7.0f, 7.0f, 7.0f };</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  std::vector<std::map<int, std::vector<T>>> inputTensors;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  std::vector<std::map<int, std::vector<T>>> outputTensors;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  inputTensors.push_back(std::map<<span class="keywordtype">int</span>, std::vector<T>> {{0, inputData1}});</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  inputTensors.push_back(std::map<<span class="keywordtype">int</span>, std::vector<T>> {{0, inputData2}});</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  outputTensors.push_back(std::map<<span class="keywordtype">int</span>, std::vector<T>> {{0, outputExpected1}});</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  outputTensors.push_back(std::map<<span class="keywordtype">int</span>, std::vector<T>> {{0, outputExpected2}});</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  AsyncThreadedEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensors, outputTensors, backends, 2);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl< DT >::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00079">ResolveType.hpp:79</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00241">INetwork.hpp:241</a></div></div> +</div><!-- fragment --> +</div> +</div> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="namespacearmnn.xhtml">armnn</a></li><li class="navelem"><a class="el" href="namespacearmnn_1_1experimental.xhtml">experimental</a></li> + <li class="footer">Generated on Tue May 24 2022 11:27:26 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |