22.02/_cl_fallback_tests_8cpp.xhtml


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<div class="textblock"><code>#include &lt;CommonTestUtils.hpp&gt;</code><br />
<code>#include &lt;GraphUtils.hpp&gt;</code><br />
<code>#include &lt;doctest/doctest.h&gt;</code><br />
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<p class="definition">Definition at line <a class="el" href="_cl_fallback_tests_8cpp_source.xhtml#l00012">12</a> of file <a class="el" href="_cl_fallback_tests_8cpp_source.xhtml">ClFallbackTests.cpp</a>.</p>

<p class="reference">References <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">IConnectableLayer::BackendSelectionHint()</a>, <a class="el" href="_graph_utils_8cpp_source.xhtml#l00068">CheckOrder()</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">IOutputSlot::Connect()</a>, <a class="el" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::CpuAcc</a>, <a class="el" href="_runtime_8cpp_source.xhtml#l00040">IRuntime::Create()</a>, <a class="el" href="_network_8cpp_source.xhtml#l00492">INetwork::Create()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00274">Layer::GetBackendId()</a>, <a class="el" href="_graph_utils_8cpp_source.xhtml#l00022">GetFirstLayerWithName()</a>, <a class="el" href="_test_utils_8cpp_source.xhtml#l00047">armnn::GetGraphForTesting()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">IConnectableLayer::GetInputSlot()</a>, <a class="el" href="_profiling_8cpp_source.xhtml#l00568">ProfilerManager::GetInstance()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00196">TensorInfo::GetNumElements()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">IConnectableLayer::GetOutputSlot()</a>, <a class="el" href="_profiling_8cpp_source.xhtml#l00580">ProfilerManager::GetProfiler()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00270">Layer::GetType()</a>, <a class="el" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::GpuAcc</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_i_network_8hpp_source.xhtml#l00230">OptimizerOptions::m_ImportEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00378">Pooling2dDescriptor::m_PoolHeight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00376">Pooling2dDescriptor::m_PoolWidth</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00380">Pooling2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00382">Pooling2dDescriptor::m_StrideY</a>, <a class="el" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">armnn::Malloc</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">armnn::MemCopy</a>, <a class="el" href="_network_8cpp_source.xhtml#l01680">armnn::Optimize()</a>, <a class="el" href="_profiling_8cpp_source.xhtml#l00605">IProfiler::Print()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00516">TensorInfo::SetConstant()</a>, and <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">IOutputSlot::SetTensorInfo()</a>.</p>
<div class="fragment"><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;{</div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;TEST_CASE(<span class="stringliteral">&quot;ClImportEnabledFallbackToNeon&quot;</span>)</div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;{</div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;    <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;    <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;    <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;    <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="l00023"></a><span class="lineno">   23</span>&#160;</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net-&gt;AddInputLayer(0, <span class="stringliteral">&quot;input0&quot;</span>);</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net-&gt;AddInputLayer(1, <span class="stringliteral">&quot;input1&quot;</span>);</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net-&gt;AddInputLayer(2, <span class="stringliteral">&quot;input2&quot;</span>);</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net-&gt;AddAdditionLayer(<span class="stringliteral">&quot;add&quot;</span>);</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net-&gt;AddSubtractionLayer(<span class="stringliteral">&quot;sub&quot;</span>);</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net-&gt;AddOutputLayer(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;    input0-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;    input1-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;    input2-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    add-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;    sub-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 4, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;    info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;    input0-&gt;<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>(info);</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    input1-&gt;<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>(info);</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    input2-&gt;<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>(info);</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    add-&gt;<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>(info);</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    sub-&gt;<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>(info);</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;    std::vector&lt;BackendId&gt; backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> };</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    <span class="comment">// Use BackendSelectionHint to specify CpuAcc for Subtraction layer</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">BackendSelectionHint</a>(backends[1]);</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    <span class="comment">// optimize the network</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime-&gt;GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>&amp; graph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNet.get());</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input0&quot;</span>);</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input1&quot;</span>);</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input2&quot;</span>);</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;add&quot;</span>);</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;[ add (0) -&gt; sub (1) ]&quot;</span>);</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;sub&quot;</span>);</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    CHECK((layer4-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    CHECK((layer5-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> ));</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> netId;</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    std::string ignoredErrorMessage;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">false</span>, <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">MemorySource::Malloc</a>, <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">MemorySource::Malloc</a>);</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    runtime-&gt;LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    <span class="comment">// Creates structures for input &amp; output</span></div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    std::vector&lt;float&gt; inputValue0</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    {</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    };</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    std::vector&lt;float&gt; inputValue1</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    {</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    };</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    std::vector&lt;float&gt; inputData2</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    {</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    };</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    std::vector&lt;float&gt; outputData(16);</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    std::vector&lt;float&gt; expectedOutput</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    {</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f, 11.0f, 9.0f, 7.0f, 5.0f</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    };</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="comment">// Prepare aligned data</span></div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements = info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="keywordtype">size_t</span> totalBytes = numElements * <span class="keyword">sizeof</span>(float);</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> alignment = 64;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <span class="keywordtype">size_t</span> space = totalBytes + alignment + alignment;</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keyword">auto</span> inputData0 = std::make_unique&lt;uint8_t[]&gt;(space);</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="keywordtype">void</span>* alignedInputPtr0 = inputData0.get();</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    CHECK(std::align(alignment, totalBytes, alignedInputPtr0, space));</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="keyword">auto</span>* intputPtr0 = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(alignedInputPtr0);</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    std::copy(inputValue0.begin(), inputValue0.end(), intputPtr0);</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="keyword">auto</span> inputData1 = std::make_unique&lt;uint8_t[]&gt;(space);</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="keywordtype">void</span>* alignedInputPtr1 = inputData1.get();</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    CHECK(std::align(alignment, totalBytes, alignedInputPtr1, space));</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <span class="keyword">auto</span>* intputPtr1 = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(alignedInputPtr1);</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160; 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   };</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    {</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime-&gt;GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    };</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    runtime-&gt;GetProfiler(netId)-&gt;EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160; 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   std::string dump = ss.str();</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    <span class="comment">// Executed Subtraction using CpuAcc</span></div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    std::size_t found = dump.find(<span class="stringliteral">&quot;NeonSubtractionWorkload_Execute&quot;</span>);</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    CHECK(found != std::string::npos);</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    <span class="comment">// Contain CopyMemGeneric</span></div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    found = dump.find(<span class="stringliteral">&quot;CopyMemGeneric&quot;</span>);</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    CHECK(found != std::string::npos);</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    CHECK(outputData == expectedOutput);</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    runtime-&gt;UnloadNetwork(netId);</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;}</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;TEST_CASE(<span class="stringliteral">&quot;ClImportDisabledFallbackToNeon&quot;</span>)</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;{</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    <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="l00170"></a><span class="lineno">  170</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net-&gt;AddInputLayer(0, <span class="stringliteral">&quot;input0&quot;</span>);</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net-&gt;AddInputLayer(1, <span class="stringliteral">&quot;input1&quot;</span>);</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net-&gt;AddInputLayer(2, <span class="stringliteral">&quot;input2&quot;</span>);</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net-&gt;AddAdditionLayer(<span class="stringliteral">&quot;add&quot;</span>);</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net-&gt;AddSubtractionLayer(<span class="stringliteral">&quot;sub&quot;</span>);</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net-&gt;AddOutputLayer(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    input0-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    input1-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    input2-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    add-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    sub-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> info = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    input0-&gt;<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>(info);</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    input1-&gt;<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>(info);</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    input2-&gt;<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>(info);</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    add-&gt;<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>(info);</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    sub-&gt;<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>(info);</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    std::vector&lt;BackendId&gt; backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>, Compute::CpuAcc };</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;    <span class="comment">// Use BackendSelectionHint to specify CpuAcc for Subtraction layer</span></div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">BackendSelectionHint</a>(backends[1]);</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    <span class="comment">// optimize the network</span></div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime-&gt;GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>&amp; graph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNet.get());</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input0&quot;</span>);</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input1&quot;</span>);</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input2&quot;</span>);</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;add&quot;</span>);</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;[ add (0) -&gt; sub (1) ]&quot;</span>);</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;sub&quot;</span>);</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    CHECK((layer4-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    CHECK((layer5-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> ));</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> netId;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    runtime-&gt;LoadNetwork(netId, std::move(optNet));</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <span class="comment">// Creates structures for input &amp; output</span></div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    std::vector&lt;float&gt; inputData0</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    {</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    };</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    std::vector&lt;float&gt; inputData1</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    {</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    };</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    std::vector&lt;float&gt; inputData2</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    {</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    };</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    std::vector&lt;float&gt; outputData(12);</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    std::vector&lt;float&gt; expectedOutput</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    {</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    };</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    {</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    };</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    {</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime-&gt;GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    };</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    runtime-&gt;GetProfiler(netId)-&gt;EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <span class="comment">// Do the inference</span></div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    runtime-&gt;EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;    <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>&amp; profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    std::stringstream ss;</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-&gt;<a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    std::string dump = ss.str();</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    <span class="comment">// Executed Subtraction using CpuAcc</span></div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    std::size_t found = dump.find(<span class="stringliteral">&quot;NeonSubtractionWorkload_Execute&quot;</span>);</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    CHECK(found != std::string::npos);</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <span class="comment">// Contain CopyMemGeneric</span></div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    found = dump.find(<span class="stringliteral">&quot;CopyMemGeneric&quot;</span>);</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    CHECK(found != std::string::npos);</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    CHECK(outputData == expectedOutput);</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;}</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;TEST_CASE(<span class="stringliteral">&quot;ClImportEnabledFallbackSubgraphToNeon&quot;</span>)</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;{</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    <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="l00293"></a><span class="lineno">  293</span>&#160;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 2;</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net-&gt;AddInputLayer(0, <span class="stringliteral">&quot;input0&quot;</span>);</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net-&gt;AddInputLayer(1, <span class="stringliteral">&quot;input1&quot;</span>);</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net-&gt;AddInputLayer(2, <span class="stringliteral">&quot;input2&quot;</span>);</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net-&gt;AddAdditionLayer(<span class="stringliteral">&quot;add&quot;</span>);</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net-&gt;AddSubtractionLayer(<span class="stringliteral">&quot;sub&quot;</span>);</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net-&gt;AddPooling2dLayer(desc, <span class="stringliteral">&quot;pooling&quot;</span>);</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net-&gt;AddOutputLayer(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    input0-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    input1-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    input2-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    add-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    sub-&gt;<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>(pooling-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    pooling-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> info = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 4, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> poolingInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 2, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    input0-&gt;<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>(info);</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    input1-&gt;<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>(info);</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    input2-&gt;<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>(info);</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    add-&gt;<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>(info);</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    sub-&gt;<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>(info);</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    pooling-&gt;<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>(poolingInfo);</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    std::vector&lt;BackendId&gt; backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>, Compute::CpuAcc };</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    <span class="comment">// Use BackendSelectionHint to specify CpuAcc for Subtraction layer</span></div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">BackendSelectionHint</a>(backends[1]);</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    <span class="comment">// optimize the network</span></div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime-&gt;GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>&amp; graph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNet.get());</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input0&quot;</span>);</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;input1&quot;</span>);</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160; 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sub (1) ]&quot;</span>);</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;sub&quot;</span>);</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;[ sub (0) -&gt; pooling (0) ]&quot;</span>);</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer7 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;pooling&quot;</span>);</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer8 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;    <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer6, layer7));</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    CHECK(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer7, layer8));</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    CHECK((layer4-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    CHECK((layer6-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    CHECK((layer5-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> ));</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> netId;</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    std::string ignoredErrorMessage;</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">false</span>, <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">MemorySource::Malloc</a>, <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">MemorySource::Malloc</a>);</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    runtime-&gt;LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;    <span class="comment">// Creates structures for input &amp; output</span></div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    std::vector&lt;float&gt; inputValue0</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;    {</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;        1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    };</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    std::vector&lt;float&gt; inputValue1</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    {</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;        0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    };</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    std::vector&lt;float&gt; inputData2</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;    {</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;    };</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;    std::vector&lt;float&gt; outputData(4);</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    std::vector&lt;float&gt; expectedOutput{ 11.0f, 3.0f, -5.0f, 11.0f };</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements = info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    <span class="keywordtype">size_t</span> totalBytes = numElements * <span class="keyword">sizeof</span>(float);</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> alignment = 64;</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    <span class="keywordtype">size_t</span> space = totalBytes + alignment + alignment;</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <span class="keyword">auto</span> inputData0 = std::make_unique&lt;uint8_t[]&gt;(space);</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    <span class="keywordtype">void</span>* alignedInputPtr0 = inputData0.get();</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    CHECK(std::align(alignment, totalBytes, alignedInputPtr0, space));</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    <span class="keyword">auto</span>* intputPtr0 = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(alignedInputPtr0);</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    std::copy(inputValue0.begin(), inputValue0.end(), intputPtr0);</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    <span class="keyword">auto</span> inputData1 = std::make_unique&lt;uint8_t[]&gt;(space);</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    <span class="keywordtype">void</span>* alignedInputPtr1 = inputData1.get();</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;    CHECK(std::align(alignment, totalBytes, alignedInputPtr1, space));</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="keyword">auto</span>* intputPtr1 = <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(alignedInputPtr1);</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    std::copy(inputValue1.begin(), inputValue1.end(), intputPtr1);</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;    <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    {</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;        { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 0), alignedInputPtr0) },</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;        { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 1), alignedInputPtr1) },</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;        { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    };</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    {</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;        { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime-&gt;GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;    };</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;    runtime-&gt;GetProfiler(netId)-&gt;EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    <span class="comment">// Do the inference</span></div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    runtime-&gt;EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160; 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   <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    <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="l00455"></a><span class="lineno">  455</span>&#160;</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net-&gt;AddInputLayer(0, <span class="stringliteral">&quot;input0&quot;</span>);</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net-&gt;AddInputLayer(1, <span class="stringliteral">&quot;input1&quot;</span>);</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160; 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   input2-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;    add-&gt;<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>(sub-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    sub-&gt;<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>(pooling-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    pooling-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> info = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> poolingInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    input0-&gt;<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>(info);</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    input1-&gt;<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>(info);</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    input2-&gt;<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>(info);</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    add-&gt;<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>(info);</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    sub-&gt;<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>(info);</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    pooling-&gt;<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>(poolingInfo);</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160; 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pooling (0) ]&quot;</span>);</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer7 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;pooling&quot;</span>);</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer8 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160; 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   CHECK((layer6-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;    <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;    CHECK((layer5-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> ));</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;    <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> netId;</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    runtime-&gt;LoadNetwork(netId, std::move(optNet));</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    <span class="comment">// Creates structures for input &amp; output</span></div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;    std::vector&lt;float&gt; inputData0</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    {</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;        1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    };</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    std::vector&lt;float&gt; inputData1</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    {</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;        0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;    };</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;    std::vector&lt;float&gt; inputData2</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    {</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;        12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    };</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    std::vector&lt;float&gt; outputData(2);</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;    std::vector&lt;float&gt; expectedOutput{ 11.0f, -1.0f };</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;    <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;    {</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;        { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;        { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;        { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime-&gt;GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;    };</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;    {</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;        { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime-&gt;GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    };</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;    runtime-&gt;GetProfiler(netId)-&gt;EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;    <span class="comment">// Do the inference</span></div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;    runtime-&gt;EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;    <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;    <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>&amp; profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;    std::stringstream ss;</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;    profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-&gt;<a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;    std::string dump = ss.str();</div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;    <span class="comment">// Executed Subtraction using CpuAcc</span></div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;    std::size_t found = dump.find(<span class="stringliteral">&quot;NeonSubtractionWorkload_Execute&quot;</span>);</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    CHECK(found != std::string::npos);</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160; 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   <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;    CHECK(outputData == expectedOutput);</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;}</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;}</div><div class="ttc" id="classarmnn_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 &amp;options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00040">Runtime.cpp:40</a></div></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="_graph_utils_8cpp_xhtml_a5f17e02e0054dac0a691685a0464ed36"><div class="ttname"><a href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a></div><div class="ttdeci">armnn::Layer * GetFirstLayerWithName(armnn::Graph &amp;graph, const std::string &amp;name)</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00022">GraphUtils.cpp:22</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml_a93857080c2523bf3395e7aa7e6024d5c"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a></div><div class="ttdeci">static ProfilerManager &amp; GetInstance()</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00568">Profiling.cpp:568</a></div></div>
<div class="ttc" id="_graph_utils_8cpp_xhtml_a21d963c71be62057ed99b5007e7bbbfd"><div class="ttname"><a href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a></div><div class="ttdeci">bool CheckOrder(const armnn::Graph &amp;graph, const armnn::Layer *first, const armnn::Layer *second)</div><div class="ttdoc">Checks that first comes before second in the order. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00068">GraphUtils.cpp:68</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a833170f92e96b3ef414b6cf6e5720d2b"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">armnn::IConnectableLayer::BackendSelectionHint</a></div><div class="ttdeci">virtual void BackendSelectionHint(Optional&lt; BackendId &gt; backend)=0</div><div class="ttdoc">Provide a hint for the optimizer as to which backend to prefer for this layer. </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="structarmnn_1_1_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00376">Descriptors.hpp:376</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&lt; IRuntime, void(*)(IRuntime *runtime)&gt; IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00031">IRuntime.hpp:31</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_profiler_xhtml_a038bb767bbc6abc0ee0d9b509616b896"><div class="ttname"><a href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">armnn::IProfiler::Print</a></div><div class="ttdeci">void Print(std::ostream &amp;outStream) const</div><div class="ttdoc">Print stats for events in JSON Format to the given output stream. </div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00605">Profiling.cpp:605</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&lt; std::pair&lt; LayerBindingId, class ConstTensor &gt; &gt; 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="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_pooling2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00380">Descriptors.hpp:380</a></div></div>
<div class="ttc" id="classarmnn_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 &amp;tensorInfo)=0</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#l00033">IRuntime.hpp:33</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml_a3756986bc88b9b212d3f983c70c5c129"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">armnn::ProfilerManager::GetProfiler</a></div><div class="ttdeci">IProfiler * GetProfiler()</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00580">Profiling.cpp:580</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="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00378">Descriptors.hpp:378</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 &amp;network, const std::vector&lt; BackendId &gt; &amp;backendPreferences, const IDeviceSpec &amp;deviceSpec, const OptimizerOptions &amp;options=OptimizerOptions(), Optional&lt; std::vector&lt; std::string &gt; &amp;&gt; 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#l01680">Network.cpp:1680</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#l00025">IRuntime.hpp:25</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&lt; std::pair&lt; LayerBindingId, class Tensor &gt; &gt; 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="classarmnn_1_1_layer_xhtml_ad8e15c530c929ab823d89ae9fd2d3f11"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">armnn::Layer::GetType</a></div><div class="ttdeci">LayerType GetType() const override</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00270">Layer.hpp:270</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">armnn::LayerType::MemCopy</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&lt; IOptimizedNetwork, void(*)(IOptimizedNetwork *network)&gt; 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="classarmnn_1_1_profiler_manager_xhtml"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml">armnn::ProfilerManager</a></div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00111">Profiling.hpp:111</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523"><div class="ttname"><a href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">armnn::MemorySource::Malloc</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a></div><div class="ttdoc">GPU Execution: OpenCL: ArmCompute. </div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a></div><div class="ttdoc">ArmNN performs an optimization on each model/network before it gets loaded for execution. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00137">INetwork.hpp:137</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_afdb1d37740e7a083b625d669588b6a0e"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">armnn::Layer::GetBackendId</a></div><div class="ttdeci">const BackendId &amp; GetBackendId() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00274">Layer.hpp:274</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00030">Graph.hpp:30</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml_a05c1bba6ba3ecc1339d4c4c10c0d8890"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">armnn::OptimizerOptions::m_ImportEnabled</a></div><div class="ttdeci">bool m_ImportEnabled</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00230">INetwork.hpp:230</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#l00075">IRuntime.hpp:75</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a6a2659750d6161b693d0e51616791959"><div class="ttname"><a href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">armnn::GetGraphForTesting</a></div><div class="ttdeci">Graph &amp; GetGraphForTesting(IOptimizedNetwork *optNet)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00047">TestUtils.cpp:47</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a></div><div class="ttdoc">CPU Execution: NEON: ArmCompute. </div></div>
<div class="ttc" id="classarmnn_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 &amp; 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="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#l00516">Tensor.cpp:516</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_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 &amp; 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="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr&lt; INetwork, void(*)(INetwork *network)&gt; 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_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 &amp;destination)=0</div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00332">Descriptors.hpp:332</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#l00492">Network.cpp:492</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00382">Descriptors.hpp:382</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00215">Layer.hpp:215</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00196">Tensor.hpp:196</a></div></div>
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