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<div class="title">AsyncExecutionSample.cpp</div> </div>
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<p>Yet another variant of the SimpleSample application. In this little sample app you will be shown how to run a network multiple times asynchronously.</p>
<dl class="section note"><dt>Note</dt><dd>This is currently an experimental interface</dd></dl>
<div class="fragment"><div class="line"><span class="comment">//</span></div>
<div class="line"><span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div>
<div class="line"><span class="comment">// SPDX-License-Identifier: MIT</span></div>
<div class="line"><span class="comment">//</span></div>
<div class="line"><span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div>
<div class="line"><span class="preprocessor">#include <<a class="code" href="_i_runtime_8hpp.xhtml">armnn/IRuntime.hpp</a>></span></div>
<div class="line"><span class="preprocessor">#include <<a class="code" href="_utils_8hpp.xhtml">armnn/Utils.hpp</a>></span></div>
<div class="line"><span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div>
<div class="line"> </div>
<div class="line"><span class="preprocessor">#include <iostream></span></div>
<div class="line"><span class="preprocessor">#include <thread></span></div>
<div class="line"><span class="comment"></span> </div>
<div class="line"><span class="comment">/// A simple example of using the ArmNN SDK API to run a network multiple times with different inputs in an asynchronous</span></div>
<div class="line"><span class="comment">/// manner.</span></div>
<div class="line"><span class="comment">///</span></div>
<div class="line"><span class="comment">/// Background info: The usual runtime->EnqueueWorkload, which is used to trigger the execution of a network, is not</span></div>
<div class="line"><span class="comment">/// thread safe. Each workload has memory assigned to it which would be overwritten by each thread.</span></div>
<div class="line"><span class="comment">/// Before we added support for this you had to load a network multiple times to execute it at the</span></div>
<div class="line"><span class="comment">/// same time. Every time a network is loaded, it takes up memory on your device. Making the</span></div>
<div class="line"><span class="comment">/// execution thread safe helps to reduce the memory footprint for concurrent executions significantly.</span></div>
<div class="line"><span class="comment">/// This example shows you how to execute a model concurrently (multiple threads) while still only</span></div>
<div class="line"><span class="comment">/// loading it once.</span></div>
<div class="line"><span class="comment">///</span></div>
<div class="line"><span class="comment">/// As in most of our simple samples, the network in this example will ask the user for a single input number for each</span></div>
<div class="line"><span class="comment">/// execution of the network.</span></div>
<div class="line"><span class="comment">/// The network consists of a single fully connected layer with a single neuron. The neurons weight is set to 1.0f</span></div>
<div class="line"><span class="comment">/// to produce an output number that is the same as the input.</span></div>
<div class="line"><span class="comment"></span><span class="keywordtype">int</span> <a name="a0"></a><a class="code" href="_armnn_converter_8cpp.xhtml#a0ddf1224851353fc92bfbff6f499fa97">main</a>()</div>
<div class="line">{</div>
<div class="line"> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// The first part of this code is very similar to the SimpleSample.cpp you should check it out for comparison</span></div>
<div class="line"> <span class="comment">// The interesting part starts when the graph is loaded into the runtime</span></div>
<div class="line"> </div>
<div class="line"> std::vector<float> inputs;</div>
<div class="line"> <span class="keywordtype">float</span> number1;</div>
<div class="line"> std::cout << <span class="stringliteral">"Please enter a number for the first iteration: "</span> << std::endl;</div>
<div class="line"> std::cin >> number1;</div>
<div class="line"> <span class="keywordtype">float</span> number2;</div>
<div class="line"> std::cout << <span class="stringliteral">"Please enter a number for the second iteration: "</span> << std::endl;</div>
<div class="line"> std::cin >> number2;</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Turn on logging to standard output</span></div>
<div class="line"> <span class="comment">// This is useful in this sample so that users can learn more about what is going on</span></div>
<div class="line"> <a name="a1"></a><a class="code" href="namespacearmnn.xhtml#aa59f7a819c3e29d10ffc41e5c0616872">ConfigureLogging</a>(<span class="keyword">true</span>, <span class="keyword">false</span>, <a name="a2"></a><a class="code" href="namespacearmnn.xhtml#a93a3ba385cad27c4774e5fe64c025d3da0eaadb4fcb48a0a0ed7bc9868be9fbaa">LogSeverity::Warning</a>);</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Construct ArmNN network</span></div>
<div class="line"> <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkIdentifier;</div>
<div class="line"> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> myNetwork = <a name="a3"></a><a class="code" href="classarmnn_1_1_i_network.xhtml#a41ce159095e95f7cd4174ce5d4662697">INetwork::Create</a>();</div>
<div class="line"> </div>
<div class="line"> <span class="keywordtype">float</span> weightsData[] = {1.0f}; <span class="comment">// Identity</span></div>
<div class="line"> <a name="_a4"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo(<a name="_a5"></a><a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 1}), <a name="a6"></a><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div>
<div class="line"> weightsInfo.<a name="a7"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div>
<div class="line"> <a name="_a8"></a><a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsData);</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Constant layer that now holds weights data for FullyConnected</span></div>
<div class="line"> <a name="_a9"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> constantWeightsLayer = myNetwork->AddConstantLayer(weights, <span class="stringliteral">"const weights"</span>);</div>
<div class="line"> </div>
<div class="line"> <a name="_a10"></a><a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> fullyConnectedDesc;</div>
<div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> fullyConnectedLayer = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc,</div>
<div class="line"> <span class="stringliteral">"fully connected"</span>);</div>
<div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <a name="_a11"></a><a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a> = myNetwork->AddInputLayer(0);</div>
<div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <a name="_a12"></a><a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a> = myNetwork->AddOutputLayer(0);</div>
<div class="line"> </div>
<div class="line"> <a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>-><a name="a13"></a><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a name="a14"></a><a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(fullyConnectedLayer-><a name="a15"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div>
<div class="line"> constantWeightsLayer-><a name="a16"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a17"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div>
<div class="line"> fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>-><a name="a18"></a><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Create ArmNN runtime</span></div>
<div class="line"> <a name="_a19"></a><a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options; <span class="comment">// default options</span></div>
<div class="line"> <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> run = <a name="a20"></a><a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options);</div>
<div class="line"> </div>
<div class="line"> <span class="comment">//Set the tensors in the network.</span></div>
<div class="line"> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div>
<div class="line"> <a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a name="a21"></a><a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputTensorInfo);</div>
<div class="line"> </div>
<div class="line"> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div>
<div class="line"> fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a22"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div>
<div class="line"> constantWeightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightsInfo);</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Optimise ArmNN network</span></div>
<div class="line"> <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a name="a23"></a><a class="code" href="namespacearmnn.xhtml#aa42e128b41f4e966fc901f9bf42c5a1c">Optimize</a>(*myNetwork, {<a name="a24"></a><a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run->GetDeviceSpec());</div>
<div class="line"> <span class="keywordflow">if</span> (!optNet)</div>
<div class="line"> {</div>
<div class="line"> <span class="comment">// This shouldn't happen for this simple sample, with reference backend.</span></div>
<div class="line"> <span class="comment">// But in general usage Optimize could fail if the hardware at runtime cannot</span></div>
<div class="line"> <span class="comment">// support the model that has been provided.</span></div>
<div class="line"> std::cerr << <span class="stringliteral">"Error: Failed to optimise the input network."</span> << std::endl;</div>
<div class="line"> <span class="keywordflow">return</span> 1;</div>
<div class="line"> }</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Load graph into runtime.</span></div>
<div class="line"> std::string errmsg; <span class="comment">// To hold an eventual error message if loading the network fails</span></div>
<div class="line"> <span class="comment">// Add network properties to enable async execution. The MemorySource::Undefined variables indicate</span></div>
<div class="line"> <span class="comment">// that neither inputs nor outputs will be imported. Importing will be covered in another example.</span></div>
<div class="line"> <a name="_a25"></a><a class="code" href="structarmnn_1_1_i_network_properties.xhtml">armnn::INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <a name="a26"></a><a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277aec0fc0100c4fc1ce4eea230c3dc10360">MemorySource::Undefined</a>, <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277aec0fc0100c4fc1ce4eea230c3dc10360">MemorySource::Undefined</a>);</div>
<div class="line"> run->LoadNetwork(networkIdentifier,</div>
<div class="line"> std::move(optNet),</div>
<div class="line"> errmsg,</div>
<div class="line"> networkProperties);</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Creates structures for inputs and outputs. A vector of float for each execution.</span></div>
<div class="line"> std::vector<std::vector<float>> inputData{{number1}, {number2}};</div>
<div class="line"> std::vector<std::vector<float>> outputData;</div>
<div class="line"> outputData.resize(2, std::vector<float>(1));</div>
<div class="line"> </div>
<div class="line"> inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);</div>
<div class="line"> inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div>
<div class="line"> std::vector<InputTensors> inputTensors</div>
<div class="line"> {</div>
<div class="line"> {{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, inputData[0].data())}},</div>
<div class="line"> {{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, inputData[1].data())}}</div>
<div class="line"> };</div>
<div class="line"> std::vector<OutputTensors> outputTensors</div>
<div class="line"> {</div>
<div class="line"> {{0, <a name="_a27"></a><a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputData[0].data())}},</div>
<div class="line"> {{0, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputData[1].data())}}</div>
<div class="line"> };</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Lambda function to execute the network. We use it as thread function.</span></div>
<div class="line"> <span class="keyword">auto</span> execute = [&](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> executionIndex)</div>
<div class="line"> {</div>
<div class="line"> <span class="keyword">auto</span> memHandle = run->CreateWorkingMemHandle(networkIdentifier);</div>
<div class="line"> run->Execute(*memHandle, inputTensors[executionIndex], outputTensors[executionIndex]);</div>
<div class="line"> };</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Prepare some threads and let each execute the network with a different input</span></div>
<div class="line"> std::vector<std::thread> threads;</div>
<div class="line"> <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputTensors.size(); ++i)</div>
<div class="line"> {</div>
<div class="line"> threads.emplace_back(std::thread(execute, i));</div>
<div class="line"> }</div>
<div class="line"> </div>
<div class="line"> <span class="comment">// Wait for the threads to finish</span></div>
<div class="line"> <span class="keywordflow">for</span> (std::thread& t : threads)</div>
<div class="line"> {</div>
<div class="line"> <span class="keywordflow">if</span>(t.joinable())</div>
<div class="line"> {</div>
<div class="line"> t.join();</div>
<div class="line"> }</div>
<div class="line"> }</div>
<div class="line"> </div>
<div class="line"> std::cout << <span class="stringliteral">"Your numbers were "</span> << outputData[0][0] << <span class="stringliteral">" and "</span> << outputData[1][0] << std::endl;</div>
<div class="line"> <span class="keywordflow">return</span> 0;</div>
<div class="line"> </div>
<div class="line">}</div>
</div><!-- fragment --> </div><!-- contents -->
</div><!-- doc-content -->
<div class="ttc" id="anamespacearmnn_xhtml_a93a3ba385cad27c4774e5fe64c025d3da0eaadb4fcb48a0a0ed7bc9868be9fbaa"><div class="ttname"><a href="namespacearmnn.xhtml#a93a3ba385cad27c4774e5fe64c025d3da0eaadb4fcb48a0a0ed7bc9868be9fbaa">armnn::LogSeverity::Warning</a></div><div class="ttdeci">@ Warning</div></div>
<div class="ttc" id="anamespacearmnn_xhtml_a14fcd7f88d11cea0a018269dca5f9277aec0fc0100c4fc1ce4eea230c3dc10360"><div class="ttname"><a href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277aec0fc0100c4fc1ce4eea230c3dc10360">armnn::MemorySource::Undefined</a></div><div class="ttdeci">@ Undefined</div></div>
<div class="ttc" id="anamespacearmnn_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#l00328">INetwork.hpp:328</a></div></div>
<div class="ttc" id="a_utils_8hpp_xhtml"><div class="ttname"><a href="_utils_8hpp.xhtml">Utils.hpp</a></div></div>
<div class="ttc" id="aclassarmnn_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#l00068">INetwork.hpp:68</a></div></div>
<div class="ttc" id="astructarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00495">Descriptors.hpp:495</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_xhtml_a7e5c5771d741dd5473989047a9314728"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">armnn::OutputSlot::SetTensorInfo</a></div><div class="ttdeci">void SetTensorInfo(const TensorInfo &tensorInfo) override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00087">Layer.cpp:87</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_input_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs).</div><div class="ttdef"><b>Definition:</b> <a href="_input_layer_8hpp_source.xhtml#l00013">InputLayer.hpp:13</a></div></div>
<div class="ttc" id="aclassarmnn_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="anamespacearmnn_xhtml_aa59f7a819c3e29d10ffc41e5c0616872"><div class="ttname"><a href="namespacearmnn.xhtml#aa59f7a819c3e29d10ffc41e5c0616872">armnn::ConfigureLogging</a></div><div class="ttdeci">void ConfigureLogging(bool printToStandardOutput, bool printToDebugOutput, LogSeverity severity)</div><div class="ttdoc">Configures the logging behaviour of the ARMNN library.</div><div class="ttdef"><b>Definition:</b> <a href="_utils_8cpp_source.xhtml#l00018">Utils.cpp:18</a></div></div>
<div class="ttc" id="astructarmnn_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#l00085">IRuntime.hpp:85</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index.</div></div>
<div class="ttc" id="anamespacearmnn_xhtml_aa42e128b41f4e966fc901f9bf42c5a1c"><div class="ttname"><a href="namespacearmnn.xhtml#aa42e128b41f4e966fc901f9bf42c5a1c">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptionsOpaque &options=OptimizerOptionsOpaque(), 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#l02003">Network.cpp:2003</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div>
<div class="ttc" id="aclassarmnn_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 class="ttc" id="a_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_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="aclassarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index.</div></div>
<div class="ttc" id="astructarmnn_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#l00043">IRuntime.hpp:43</a></div></div>
<div class="ttc" id="aclassarmnn_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="aclassarmnn_1_1_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot & GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index.</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00326">Layer.hpp:326</a></div></div>
<div class="ttc" id="anamespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div><div class="ttdeci">@ Float32</div></div>
<div class="ttc" id="aclassarmnn_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="anamespacearmnn_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#l00035">IRuntime.hpp:35</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_xhtml_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">armnn::Layer::GetInputSlot</a></div><div class="ttdeci">const InputSlot & GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index.</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00324">Layer.hpp:324</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_i_network_xhtml_a41ce159095e95f7cd4174ce5d4662697"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a41ce159095e95f7cd4174ce5d4662697">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(const NetworkOptions &networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00652">Network.cpp:652</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_xhtml_adcfb97035799ea4c043f9ef370714815"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">armnn::OutputSlot::Connect</a></div><div class="ttdeci">int Connect(InputSlot &destination)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00112">Layer.cpp:112</a></div></div>
<div class="ttc" id="a_i_runtime_8hpp_xhtml"><div class="ttname"><a href="_i_runtime_8hpp.xhtml">IRuntime.hpp</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div>
<div class="ttc" id="a_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_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#l00327">INetwork.hpp:327</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_i_runtime_xhtml_ad44ecd3700748dc30dc4bbe34ba5bde7"><div class="ttname"><a href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a></div><div class="ttdeci">static IRuntimePtr Create(const CreationOptions &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00052">Runtime.cpp:52</a></div></div>
<div class="ttc" id="a_armnn_converter_8cpp_xhtml_a0ddf1224851353fc92bfbff6f499fa97"><div class="ttname"><a href="_armnn_converter_8cpp.xhtml#a0ddf1224851353fc92bfbff6f499fa97">main</a></div><div class="ttdeci">int main(int argc, char *argv[])</div><div class="ttdef"><b>Definition:</b> <a href="_armnn_converter_8cpp_source.xhtml#l00327">ArmnnConverter.cpp:327</a></div></div>
<div class="ttc" id="aclassarmnn_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="anamespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a></div><div class="ttdeci">@ CpuRef</div><div class="ttdoc">CPU Execution: Reference C++ kernels.</div></div>
<div class="ttc" id="anamespacearmnn_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#l00041">IRuntime.hpp:41</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs).</div><div class="ttdef"><b>Definition:</b> <a href="_output_layer_8hpp_source.xhtml#l00013">OutputLayer.hpp:13</a></div></div>
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