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<div class="title">DynamicSample.cpp</div> </div>
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<p>This is simple example that shows how to use a dynamic backend. Dynamic Backends can be compiled as standalone against Arm NN and can be loaded by Arm NN dynamically at runtime. This way you can quickly integrate new backends without having to worry or recompile Arm NN.</p>
<p>This example makes use of a very simplistic dynamic backend called 'SampleDynamic'. There is a guide that tells you more about dynamic backends and how this particular backend was created so you can create a dynamic backend yourself <a class="el" href="md_src_dynamic__r_e_a_d_m_e.xhtml">Dynamically loadable Backend</a>.</p>
<div class="fragment"><div class="line"><span class="comment">//</span></div><div class="line"><span class="comment">// Copyright © 2020 Arm Ltd. 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="comment"></span></div><div class="line"><span class="comment">/// A simple example of using the ArmNN SDK API with the standalone sample dynamic backend.</span></div><div class="line"><span class="comment">/// In this example, an addition layer is used to add 2 input tensors to produce a result output tensor.</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">// Construct ArmNN network</span></div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a> networkIdentifier;</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> myNetwork = <a name="a1"></a><a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"></div><div class="line"> <a name="_a2"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = myNetwork->AddInputLayer(0);</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = myNetwork->AddInputLayer(1);</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = myNetwork->AddAdditionLayer();</div><div class="line"> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = myNetwork->AddOutputLayer(0);</div><div class="line"></div><div class="line"> input0-><a name="a3"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a4"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-><a name="a5"></a><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"> input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"> add-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"></div><div class="line"> <a name="_a6"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(<a name="_a7"></a><a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({2, 1}), <a name="a8"></a><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"> input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a name="a9"></a><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"> input1-><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>(tensorInfo);</div><div class="line"> add-><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>(tensorInfo);</div><div class="line"></div><div class="line"> <span class="comment">// Create ArmNN runtime</span></div><div class="line"> <a name="_a10"></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">armnn::IRuntimePtr</a> run(<a name="a11"></a><a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</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">armnn::IOptimizedNetworkPtr</a> optNet = <a name="a12"></a><a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*myNetwork, {<span class="stringliteral">"SampleDynamic"</span>}, 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"> run->LoadNetwork(networkIdentifier, std::move(optNet));</div><div class="line"></div><div class="line"> <span class="comment">// input data</span></div><div class="line"> std::vector<float> input0Data</div><div class="line"> {</div><div class="line"> 5.0f, 3.0f</div><div class="line"> };</div><div class="line"> std::vector<float> input1Data</div><div class="line"> {</div><div class="line"> 10.0f, 8.0f</div><div class="line"> };</div><div class="line"> std::vector<float> outputData(2);</div><div class="line"></div><div class="line"> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);</div><div class="line"> inputTensorInfo.<a name="a13"></a><a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"> {</div><div class="line"> {0,<a name="_a14"></a><a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, input0Data.data())},</div><div class="line"> {1,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, input1Data.data())}</div><div class="line"> };</div><div class="line"> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"> {</div><div class="line"> {0,<a name="_a15"></a><a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}</div><div class="line"> };</div><div class="line"></div><div class="line"> <span class="comment">// Execute network</span></div><div class="line"> run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"></div><div class="line"> std::cout << <span class="stringliteral">"Addition operator result is {"</span> << outputData[0] << <span class="stringliteral">","</span> << outputData[1] << <span class="stringliteral">"}"</span> << std::endl;</div><div class="line"> <span class="keywordflow">return</span> 0;</div><div class="line">}</div></div><!-- fragment --> </div><!-- contents -->
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