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<div class="textblock"><code>#include &quot;<a class="el" href="_layers_fwd_8hpp_source.xhtml">LayersFwd.hpp</a>&quot;</code><br />
<code>#include &lt;<a class="el" href="_network_8hpp_source.xhtml">Network.hpp</a>&gt;</code><br />
<code>#include &lt;<a class="el" href="_test_utils_8hpp_source.xhtml">test/TestUtils.hpp</a>&gt;</code><br />
<code>#include &lt;doctest/doctest.h&gt;</code><br />
<code>#include &lt;<a class="el" href="_tensor_handle_8hpp_source.xhtml">backendsCommon/TensorHandle.hpp</a>&gt;</code><br />
<code>#include &lt;<a class="el" href="_optimizer_8hpp_source.xhtml">Optimizer.hpp</a>&gt;</code><br />
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<p class="definition">Definition at line <a class="el" href="_fold_pad_tests_8cpp_source.xhtml#l00013">13</a> of file <a class="el" href="_fold_pad_tests_8cpp_source.xhtml">FoldPadTests.cpp</a>.</p>

<p class="reference">References <a class="el" href="_graph_8hpp_source.xhtml#l00402">Graph::AddLayer()</a>, <a class="el" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::Average</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00172">Graph::cbegin()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00174">Graph::cend()</a>, <a class="el" href="_test_utils_8hpp_source.xhtml#l00021">CheckSequence()</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">IOutputSlot::Connect()</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00083">OutputSlot::Connect()</a>, <a class="el" href="_network_8cpp_source.xhtml#l00530">INetwork::Create()</a>, <a class="el" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::Exclude</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">IConnectableLayer::GetInputSlot()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00316">Layer::GetInputSlot()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">IConnectableLayer::GetOutputSlot()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00318">Layer::GetOutputSlot()</a>, <a class="el" href="_layer_with_parameters_8hpp_source.xhtml#l00018">LayerWithParameters&lt; Parameters &gt;::GetParameters()</a>, <a class="el" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::IgnoreValue</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00466">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00518">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00385">Pooling2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00468">Convolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00520">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00371">Pooling2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00383">Pooling2dDescriptor::m_PaddingMethod</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00365">Pooling2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00367">Pooling2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00369">Pooling2dDescriptor::m_PadTop</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01015">PadDescriptor::m_PadValue</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00375">Pooling2dDescriptor::m_PoolHeight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00363">Pooling2dDescriptor::m_PoolType</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00373">Pooling2dDescriptor::m_PoolWidth</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00377">Pooling2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00458">Convolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00510">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00379">Pooling2dDescriptor::m_StrideY</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00460">Convolution2dDescriptor::m_StrideY</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00512">DepthwiseConvolution2dDescriptor::m_StrideY</a>, <a class="el" href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00019">DepthwiseConvolution2dLayer::m_Weight</a>, <a class="el" href="_convolution2d_layer_8hpp_source.xhtml#l00020">Convolution2dLayer::m_Weight</a>, <a class="el" href="_optimizer_8hpp_source.xhtml#l00043">armnn::MakeOptimizations()</a>, <a class="el" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::Max</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, <a class="el" href="_optimizer_8cpp_source.xhtml#l00016">Optimizer::Pass()</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">IOutputSlot::SetTensorInfo()</a>, and <a class="el" href="_layer_8cpp_source.xhtml#l00058">OutputSlot::SetTensorInfo()</a>.</p>
<div class="fragment"><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;{</div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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_1_1optimizations.xhtml">armnn::optimizations</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;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoConvolution2dLayer&quot;</span>)</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;{</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>              graph;</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]   = {1, 2, 2, 3};</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[]  = {1, 6, 6, 3};</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = {1, 2, 3, 3};</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[]  = {1, 2, 1, 1};</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>&gt;(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;    <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;                                 {2, 2},</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;                                 {2, 2},</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;                                 {0, 0}});</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;    <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>* padLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>&gt;(padDescriptor, <span class="stringliteral">&quot;pad&quot;</span>);</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;    padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paddedInfo);</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = 1;</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = 1;</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>  = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    std::vector&lt;float&gt; weightsVector(18);</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>        weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), weightsVector);</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;    <a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>* conv2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>&gt;(convolution2dDescriptor, <span class="stringliteral">&quot;conv2d&quot;</span>);</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    conv2dLayer-&gt;<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a6266a703017d7296f87cc4923df2d725">m_Weight</a> = std::make_unique&lt;ScopedTensorHandle&gt;(weights);</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    conv2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>&gt;(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    <span class="comment">// Connect up layers - input -&gt; pad -&gt; conv2d -&gt; output</span></div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    padLayer-&gt;GetOutputSlot().Connect(conv2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    conv2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    <span class="keyword">auto</span> checkSimpleConv2d = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer)-&gt;<span class="keywordtype">bool</span> {</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer       = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayerParams = conv2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>();</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;Convolution2dLayer&gt;(layer) &amp;&amp; (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;conv2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;            (conv2dLayerParams.m_PadLeft == 0) &amp;&amp; (conv2dLayerParams.m_PadRight == 0) &amp;&amp;</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;            (conv2dLayerParams.m_PadTop == 0) &amp;&amp; (conv2dLayerParams.m_PadBottom == 0) &amp;&amp;</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;            (conv2dLayerParams.m_StrideX == 1) &amp;&amp; (conv2dLayerParams.m_StrideY == 1) &amp;&amp;</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;            (conv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &amp;&amp; (conv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    };</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;                             checkSimpleConv2d,</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a8b394ff60ed829a91f07deac476f3db2">FoldPadIntoConvolution2d</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="keyword">auto</span> checkPadFoldedIntoConv2d = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer)-&gt;<span class="keywordtype">bool</span> {</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer       = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayerParams = conv2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>();</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;Convolution2dLayer&gt;(layer) &amp;&amp; (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;folded-pad-into-conv2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;            (conv2dLayerParams.m_PadLeft == 2) &amp;&amp; (conv2dLayerParams.m_PadRight == 2) &amp;&amp;</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;            (conv2dLayerParams.m_PadTop == 2) &amp;&amp; (conv2dLayerParams.m_PadBottom == 2) &amp;&amp;</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;            (conv2dLayerParams.m_StrideX == 1) &amp;&amp; (conv2dLayerParams.m_StrideY == 1) &amp;&amp;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;            (conv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &amp;&amp; (conv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</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;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;                             checkPadFoldedIntoConv2d,</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</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;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoDepthwiseConvolution2dLayer&quot;</span>)</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;{</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>              graph;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]   = {1, 2, 2, 3};</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[]  = {1, 6, 6, 3};</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = {1, 2, 3, 3};</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[]  = {1, 2, 1, 3};</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;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>&gt;(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                                 {2, 2},</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;                                 {2, 2},</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                                 {0, 0}});</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;    <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>* padLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>&gt;(padDescriptor, <span class="stringliteral">&quot;pad&quot;</span>);</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paddedInfo);</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;    <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> depthwiseConvolution2dDescriptor;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    depthwiseConvolution2dDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    depthwiseConvolution2dDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = 1;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    depthwiseConvolution2dDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = 1;</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    depthwiseConvolution2dDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>  = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    std::vector&lt;float&gt; weightsVector(18);</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>        weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), weightsVector);</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keyword">auto</span>* depthwiseConv2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>&gt;(depthwiseConvolution2dDescriptor,</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;                                                                             <span class="stringliteral">&quot;depthwiseConv2d&quot;</span>);</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    depthwiseConv2dLayer-&gt;<a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml#a6266a703017d7296f87cc4923df2d725">m_Weight</a> = std::make_unique&lt;ScopedTensorHandle&gt;(weights);</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    depthwiseConv2dLayer-&gt;GetOutputSlot().SetTensorInfo(outputInfo);</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>&gt;(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    <span class="comment">// Connect up layers - input -&gt; pad -&gt; depthwiseConv2d -&gt; output</span></div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    padLayer-&gt;GetOutputSlot().Connect(depthwiseConv2dLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    depthwiseConv2dLayer-&gt;GetOutputSlot().Connect(output-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <span class="keyword">auto</span> checkSimpleDepthwiseConv2d = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer)-&gt;<span class="keywordtype">bool</span> {</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayer       = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayerParams = depthwiseConv2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>();</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;DepthwiseConvolution2dLayer&gt;(layer) &amp;&amp; (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;depthwiseConv2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;            (depthwiseConv2dLayerParams.m_PadLeft == 0) &amp;&amp; (depthwiseConv2dLayerParams.m_PadRight == 0) &amp;&amp;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;            (depthwiseConv2dLayerParams.m_PadTop == 0) &amp;&amp; (depthwiseConv2dLayerParams.m_PadBottom == 0) &amp;&amp;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;            (depthwiseConv2dLayerParams.m_StrideX == 1) &amp;&amp; (depthwiseConv2dLayerParams.m_StrideY == 1) &amp;&amp;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;            (depthwiseConv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &amp;&amp;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;            (depthwiseConv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    };</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;                             checkSimpleDepthwiseConv2d,</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a227e9ab5e488aa90ba462790ba0e5aec">FoldPadIntoDepthwiseConvolution2d</a>()));</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;    <span class="keyword">auto</span> checkPadFoldedIntoDepthwiseConv2d = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer)-&gt;<span class="keywordtype">bool</span> {</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayer       = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayerParams = depthwiseConv2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>();</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;DepthwiseConvolution2dLayer&gt;(layer) &amp;&amp;</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;            (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;folded-pad-into-depthwiseConv2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;            (depthwiseConv2dLayerParams.m_PadLeft == 2) &amp;&amp; (depthwiseConv2dLayerParams.m_PadRight == 2) &amp;&amp;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;            (depthwiseConv2dLayerParams.m_PadTop == 2) &amp;&amp; (depthwiseConv2dLayerParams.m_PadBottom == 2) &amp;&amp;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;            (depthwiseConv2dLayerParams.m_StrideX == 1) &amp;&amp; (depthwiseConv2dLayerParams.m_StrideY == 1) &amp;&amp;</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;            (depthwiseConv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &amp;&amp;</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;            (depthwiseConv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    };</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;                             checkPadFoldedIntoDepthwiseConv2d,</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;}</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoPooling2dLayer&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;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>              graph;</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {1, 2, 2, 3};</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[] = {1, 4, 4, 3};</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 2, 2, 3};</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>&gt;(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;                                 {0, 0}});</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>* padLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>&gt;(padDescriptor, <span class="stringliteral">&quot;pad&quot;</span>);</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paddedInfo);</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a>   = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>  = 3;</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>    = 1;</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>    = 1;</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>* pool2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>&gt;(pooling2dDescriptor, <span class="stringliteral">&quot;pool2d&quot;</span>);</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>&gt;(0, <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">// Connect up layers - input -&gt; pad -&gt; pool2d -&gt; output</span></div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    padLayer-&gt;GetOutputSlot().Connect(pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <span class="keyword">auto</span> checkSimplePool2d = [&amp;](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) {</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;Pooling2dLayer&gt;(layer) &amp;&amp; (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;pool2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;            (pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>() == pooling2dDescriptor);</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    };</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;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;                             checkSimplePool2d,</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a279d0a7c56966cea334303d48a874964">FoldPadIntoPooling2d</a>()));</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    <span class="keyword">auto</span> checkPadFoldedIntoPool2d = [&amp;](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) {</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        <span class="keywordflow">if</span> (!IsLayerOfType&lt;Pooling2dLayer&gt;(layer) || (layer-&gt;GetNameStr() != <span class="stringliteral">&quot;folded-pad-into-pool2d&quot;</span>))</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        {</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        }</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span>                pool2dLayer       = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;        <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pool2dLayerParams = pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>();</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pool2dLayerParamsNoPad = pool2dLayerParams;</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>       = 0;</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;        pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>      = 0;</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>        = 0;</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>     = 0;</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        <span class="comment">// If we fold then PaddingMethod will be set to Ignore. The original will be Exclude.</span></div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>;</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;        <span class="keywordflow">return</span> (pool2dLayerParamsNoPad == pooling2dDescriptor) &amp;&amp; (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> == 1) &amp;&amp;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;            (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> == 1) &amp;&amp; (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> == 1) &amp;&amp;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;            (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> == 1) &amp;&amp; (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> == <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">PaddingMethod::IgnoreValue</a>);</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    };</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;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;                             checkPadFoldedIntoPool2d,</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;}</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;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoPooling2d_PadWithMultipleOutputsShouldNotBeOptimized&quot;</span>)</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;    <span class="comment">// In this test case we&#39;ll setup a pad layer with two outputs. One goes to a polling layers and the other</span></div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    <span class="comment">// goes to an output layer. 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   <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>&gt;(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;                                 {0, 0}});</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;    <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>* padLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>&gt;(padDescriptor, <span class="stringliteral">&quot;pad&quot;</span>);</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paddedInfo);</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;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a>   = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>  = 3;</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>    = 1;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>    = 1;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</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;    <a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>* pool2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>&gt;(pooling2dDescriptor, <span class="stringliteral">&quot;pool2d&quot;</span>);</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</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="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>&gt;(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <span class="comment">// Connect up layers - input -&gt; pad -&gt; pool2d -&gt; output</span></div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    padLayer-&gt;GetOutputSlot().Connect(pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160; 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   <span class="keyword">auto</span> checkSimplePool2d = [&amp;](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) {</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;Pooling2dLayer&gt;(layer) &amp;&amp; (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;pool2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;            (pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>() == pooling2dDescriptor);</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    };</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <span class="comment">// Initial sequence.</span></div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;                             checkSimplePool2d,</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;,</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a279d0a7c56966cea334303d48a874964">FoldPadIntoPooling2d</a>()));</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="comment">// The network should not change.</span></div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;                             checkSimplePool2d,</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;,</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;}</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoPooling2dLayer_PoolingLayerWithExcludePaddingShouldNotTakeMorePadding&quot;</span>)</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;{</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    <span class="comment">// In this test setup input, Pad layer, Pooling layer that includes padding, output layer. The optimization</span></div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <span class="comment">// should not work as the pooling layer already includes and existing pad and specifies PaddingMethod::Exclude.</span></div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>              graph;</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {1, 2, 2, 3};</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[] = {1, 4, 4, 3};</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 2, 2, 3};</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>&gt;(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;                                 {0, 0}});</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>* padLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>&gt;(padDescriptor, <span class="stringliteral">&quot;pad&quot;</span>);</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paddedInfo);</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a>      = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>     = 3;</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>    = 3;</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>       = 1;</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>       = 1;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>    = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    <span class="comment">// Include a pad with the pooling layer. This should prevent the optimization working.</span></div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>       = 1;</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>      = 1;</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>        = 1;</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>     = 1;</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>* pool2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>&gt;(pooling2dDescriptor, <span class="stringliteral">&quot;pool2d&quot;</span>);</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>&gt;(0, <span class="stringliteral">&quot;output&quot;</span>);</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;    <span class="comment">// Connect up layers - input -&gt; pad -&gt; pool2d -&gt; output</span></div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    padLayer-&gt;GetOutputSlot().Connect(pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="keyword">auto</span> checkSimplePool2d = [&amp;](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) {</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;Pooling2dLayer&gt;(layer) &amp;&amp; (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;pool2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;            (pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>() == pooling2dDescriptor);</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;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;                             checkSimplePool2d,</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a279d0a7c56966cea334303d48a874964">FoldPadIntoPooling2d</a>()));</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <span class="comment">// The optimization should not have modified the graph.</span></div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;                             checkSimplePool2d,</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</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;</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoPooling2dLayer_MaxPoolingLayerWithLargePadValueShouldNotBeFolded&quot;</span>)</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;{</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    <span class="comment">// In this test setup input, Pad layer with a large pad value, Max Pooling layer, output layer. The optimization</span></div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="comment">// should not work as the pad value will modify the result of the max pooling layer.</span></div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>              graph;</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {1, 2, 2, 3};</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[] = {1, 4, 4, 3};</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 2, 2, 3};</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>&gt;(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</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;    <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;                                 {1, 1},</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;                                 {0, 0}});</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    <span class="comment">// For Max pooling of a float a pad value of 0 is more than enough to stop the fold happening.</span></div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;    <span class="comment">// Set this to -std::numeric_limits&lt;float&gt;::infinity() to make the fold happen.</span></div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;    padDescriptor.m_PadValue = 0;</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;    <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>* padLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>&gt;(padDescriptor, <span class="stringliteral">&quot;pad&quot;</span>);</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;    padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paddedInfo);</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a>   = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>  = 3;</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>    = 1;</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>    = 1;</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;    pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    <a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>* pool2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>&gt;(pooling2dDescriptor, <span class="stringliteral">&quot;pool2d&quot;</span>);</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>&gt;(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;    <span class="comment">// Connect up layers - input -&gt; pad -&gt; pool2d -&gt; output</span></div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;    input-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;    padLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    <span class="keyword">auto</span> checkSimplePool2d = [&amp;](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) {</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;        <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">&gt;</span>(layer);</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;        <span class="keywordflow">return</span> IsLayerOfType&lt;Pooling2dLayer&gt;(layer) &amp;&amp; (layer-&gt;GetNameStr() == <span class="stringliteral">&quot;pool2d&quot;</span>) &amp;&amp;</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;            (pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>() == pooling2dDescriptor);</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;</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;                             checkSimplePool2d,</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;    <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a279d0a7c56966cea334303d48a874964">FoldPadIntoPooling2d</a>()));</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    <span class="comment">// The optimization should not have modified the graph.</span></div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    CHECK(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;                             &amp;IsLayerOfType&lt;InputLayer&gt;,</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;                             &amp;IsLayerOfType&lt;PadLayer&gt;,</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;                             checkSimplePool2d,</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;                             &amp;IsLayerOfType&lt;OutputLayer&gt;));</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;}</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;<span class="preprocessor">#if defined(ARMNNREF_ENABLED)</span></div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoPooling2dLayer_ExecuteInferenceWithAndWithoutOptimization&quot;</span>)</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;{</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    <span class="comment">// The idea of this test to run a simple pad+pool2d network twice. Once</span></div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    <span class="comment">// with FoldPadLayerIntoPooling2dLayer enabled and a second time with it</span></div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    <span class="comment">// avoided. The output tensors of each should match.</span></div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {1, 4, 4, 2};</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[] = {1, 6, 6, 2};</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 4, 4, 2};</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;    std::vector&lt;float&gt; inputData({2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;                                  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;                                  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;                                  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;                                  18.0f, 20.0f, 24.0f, 22.0f,</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;                                  20.0f, 18.0f, 22.0f, 24.0f,</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;                                  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;                                  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;                                 });</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160; 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                                    {1, 1},</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;                                     {0, 0}});</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* padLayer = network-&gt;AddPadLayer(padDescriptor, <span class="stringliteral">&quot;Pad&quot;</span>);</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;        padLayer-&gt;GetOutputSlot(0).SetTensorInfo(paddedInfo);</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160; 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       pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>    = 1;</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;        pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>    = 1;</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;        pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pool2dLayer = network-&gt;AddPooling2dLayer(pooling2dDescriptor, <span class="stringliteral">&quot;Pool2D&quot;</span>);</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;        pool2dLayer-&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>(outputInfo);</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;        <span class="comment">// Connect layers</span></div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;        inputLayer-&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>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;        padLayer-&gt;GetOutputSlot(0).Connect(pool2dLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;        pool2dLayer-&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>(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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">// Create ArmNN runtime</span></div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a>          run              = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());    <span class="comment">// default options</span></div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;        <span class="comment">// Optimise the network</span></div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run-&gt;GetDeviceSpec());</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;        <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a>            networkIdentifier;</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;        CHECK(run-&gt;LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;        <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors{{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(run-&gt;GetInputTensorInfo(networkIdentifier, 0), inputData.data())}};</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;        <span class="comment">// Set the initial values of the data to different values to the golden data just in case the inference fails.</span></div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;        std::vector&lt;float&gt; optimizedData(32, -std::numeric_limits&lt;float&gt;::infinity());</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>      outputTensors{{0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(outputInfo, optimizedData.data())}};</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;        <span class="comment">// Execute network</span></div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;        run-&gt;EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;        <span class="comment">// Unload it.</span></div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;        run-&gt;UnloadNetwork(networkIdentifier);</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;        <span class="comment">// In this second case the pad will have two outputs, one connected to the pooling layer the second connected to</span></div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;        <span class="comment">// a second output layer. This will prevent the FoldPadLayerIntoPooling2dLayer optimization from working.</span></div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;        <span class="comment">// A previous test, FoldPadLayerIntoPooling2d_PadWithMultipleOutputsShouldNotBeOptimized, has proved that doing</span></div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;        <span class="comment">// this will avoid the optimization.</span></div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* dummyOutputLayer = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;        padLayer-&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>(dummyOutputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;        <span class="comment">// Optimize and load and execute it a second time.</span></div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;        optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run-&gt;GetDeviceSpec());</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;        CHECK(run-&gt;LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;        std::vector&lt;float&gt; goldenData(32, 0.0f);</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;        std::vector&lt;float&gt; padOutputData(72, 0.0f);</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>      goldenTensors{{0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(outputInfo, goldenData.data())},</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;                                         {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(paddedInfo, padOutputData.data())}};</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;        run-&gt;EnqueueWorkload(networkIdentifier, inputTensors, goldenTensors);</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;        <span class="comment">// Now we can compare goldenData against optimizedData. They should be the same.</span></div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;        CHECK(std::equal(goldenData.begin(), goldenData.end(), optimizedData.begin()));</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;    }</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception&amp; e)</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;    {</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;        std::cerr &lt;&lt; e.what() &lt;&lt; std::endl;</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;        <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, e.what());</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;    }</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;}</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;TEST_CASE(<span class="stringliteral">&quot;FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimization&quot;</span>)</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;{</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    <span class="comment">// The idea of this test to run a simple pad+conv2d network twice. 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The output tensors of each should match.</span></div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]   = {1, 4, 4, 3}; <span class="comment">// NHWCin</span></div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[]  = {1, 6, 6, 3};</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = {4, 2, 2, 3}; <span class="comment">// CoutHWCin</span></div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[]  = {1, 5, 5, 4}; 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                                 20.0f, 18.0f, 22.0f, 24.0f,</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;                                  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;                                  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;                                  2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;                                  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;                                  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;                                  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160; 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       <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;        inputLayer-&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>(inputInfo);</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;        <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;                                     {1, 1},</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;                                     {1, 1},</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;                                     {0, 0}});</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* padLayer = network-&gt;AddPadLayer(padDescriptor, <span class="stringliteral">&quot;Pad&quot;</span>);</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;        padLayer-&gt;GetOutputSlot(0).SetTensorInfo(paddedInfo);</div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;        <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convDescriptor;</div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>  = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = 1;</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = 1;</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;        std::vector&lt;float&gt;    weightsData  = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;                                              11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;                                              21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;                                              31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>            weightsInfo(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>           weights(weightsInfo, weightsData);</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;        std::vector&lt;float&gt;    biasVector   = {5, 6, 7, 8};</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>            biasInfo({4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>           bias(biasInfo, biasVector);</div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;        <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a> optionalBias = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(bias);</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;</div><div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* conv2dLayer = network-&gt;AddConvolution2dLayer(convDescriptor,</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;                                                                        weights,</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;                                                                        optionalBias,</div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;                                                                        <span class="stringliteral">&quot;Conv2D&quot;</span>);</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;        conv2dLayer-&gt;GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;        <span class="comment">// Connect layers</span></div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;        inputLayer-&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>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;        padLayer-&gt;GetOutputSlot(0).Connect(conv2dLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;        conv2dLayer-&gt;GetOutputSlot(0).Connect(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;        <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a>          run              = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());    <span class="comment">// default options</span></div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;        <span class="comment">// Optimise the network</span></div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run-&gt;GetDeviceSpec());</div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;        <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a>            networkIdentifier;</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;        CHECK(run-&gt;LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;        <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors{{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(run-&gt;GetInputTensorInfo(networkIdentifier, 0), inputData.data())}};</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;        <span class="comment">// Set the initial values of the data to different values to the golden data just in case the inference fails.</span></div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;        std::vector&lt;float&gt; optimizedData(100, -std::numeric_limits&lt;float&gt;::infinity());</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>      outputTensors{{0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(outputInfo, optimizedData.data())}};</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;        <span class="comment">// Execute network</span></div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;        run-&gt;EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;        <span class="comment">// Unload it.</span></div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;        run-&gt;UnloadNetwork(networkIdentifier);</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;        <span class="comment">// In this second case the pad will have two outputs, one connected to the conv layer the second connected to</span></div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;        <span class="comment">// a second output layer. This will prevent the FoldPadLayerIntoConv2dLayer optimization from working.</span></div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;        <span class="comment">// A previous test, FoldPadLayerIntoConv2d_PadWithMultipleOutputsShouldNotBeOptimized, has proved that doing</span></div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;        <span class="comment">// this will avoid the optimization.</span></div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* dummyOutputLayer = network-&gt;AddOutputLayer(1);</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;        padLayer-&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>(dummyOutputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;        <span class="comment">// Optimize and load and execute it a second time.</span></div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;        optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run-&gt;GetDeviceSpec());</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;        CHECK(run-&gt;LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;        std::vector&lt;float&gt; goldenData(100, 0.0f);</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;        std::vector&lt;float&gt; padOutputData(108, 0.0f);</div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>      goldenTensors{{0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(outputInfo, goldenData.data())},</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;                                         {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(paddedInfo, padOutputData.data())}};</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;        run-&gt;EnqueueWorkload(networkIdentifier, inputTensors, goldenTensors);</div><div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;</div><div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;        <span class="comment">// Now we can compare goldenData against optimizedData. 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                                });</div><div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;    <span class="keywordflow">try</span></div><div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;    {</div><div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;        <span class="comment">// Create a network of input, pad, pooling 2D, output.</span></div><div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;        <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network-&gt;AddInputLayer(0);</div><div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;        inputLayer-&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>(inputInfo);</div><div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;</div><div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;        <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;                                     {1, 1},</div><div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;                                     {1, 1},</div><div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;                                     {0, 0}});</div><div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* padLayer = network-&gt;AddPadLayer(padDescriptor, <span class="stringliteral">&quot;Pad&quot;</span>);</div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;        padLayer-&gt;GetOutputSlot(0).SetTensorInfo(paddedInfo);</div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;</div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;        <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> convDescriptor;</div><div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>  = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>     = 1;</div><div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>     = 1;</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;        convDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;</div><div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;        std::vector&lt;float&gt;    weightsData  = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;                                              11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,</div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;                                              21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,</div><div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;                                              31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};</div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>            weightsInfo(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>           weights(weightsInfo, weightsData);</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;        std::vector&lt;float&gt;    biasVector   = {5, 6, 7, 8, 9, 10, 11, 12, 5, 6, 7, 8};</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>            biasInfo({12}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;        <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>           bias(biasInfo, biasVector);</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;        <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a> optionalBias = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(bias);</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;</div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* conv2dLayer = network-&gt;AddDepthwiseConvolution2dLayer(convDescriptor,</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;                                                                                 weights,</div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;                                                                                 optionalBias,</div><div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;                                                                                 <span class="stringliteral">&quot;DepthwiseConv2D&quot;</span>);</div><div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;        conv2dLayer-&gt;GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;</div><div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network-&gt;AddOutputLayer(0);</div><div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;</div><div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;        <span class="comment">// Connect layers</span></div><div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;        inputLayer-&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>(padLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;        padLayer-&gt;GetOutputSlot(0).Connect(conv2dLayer-&gt;GetInputSlot(0));</div><div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;        conv2dLayer-&gt;GetOutputSlot(0).Connect(outputLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;</div><div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;        <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a>          run              = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());    <span class="comment">// default options</span></div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;        <span class="comment">// Optimise the network</span></div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run-&gt;GetDeviceSpec());</div><div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;        <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a>            networkIdentifier;</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;        CHECK(run-&gt;LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;        <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors{{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(run-&gt;GetInputTensorInfo(networkIdentifier, 0), inputData.data())}};</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;        <span class="comment">// Set the initial values of the data to different values to the golden data just in case the inference fails.</span></div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;        std::vector&lt;float&gt; optimizedData(300, -std::numeric_limits&lt;float&gt;::infinity());</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;        <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>      outputTensors{{0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(outputInfo, optimizedData.data())}};</div><div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;        <span class="comment">// Execute network</span></div><div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;        run-&gt;EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;        <span class="comment">// Unload it.</span></div><div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;        run-&gt;UnloadNetwork(networkIdentifier);</div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;</div><div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;        <span class="comment">// In this second case the pad will have two outputs, one connected to the conv layer the second connected to</span></div><div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;        <span class="comment">// a second output layer. 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<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00468">Descriptors.hpp:468</a></div></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#l00039">Runtime.cpp:39</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#l00061">INetwork.hpp:61</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00371">Descriptors.hpp:371</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00518">Descriptors.hpp:518</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_with_parameters_xhtml_a502c06a1b13e6d90a6cbf47c081f1444"><div class="ttname"><a href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">armnn::LayerWithParameters::GetParameters</a></div><div class="ttdeci">const Parameters &amp; GetParameters() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_with_parameters_8hpp_source.xhtml#l00018">LayerWithParameters.hpp:18</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a></div><div class="ttdoc">CPU Execution: Reference C++ kernels. </div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00365">Descriptors.hpp:365</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a8b394ff60ed829a91f07deac476f3db2"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a8b394ff60ed829a91f07deac476f3db2">armnn::optimizations::FoldPadIntoConvolution2d</a></div><div class="ttdeci">OptimizeForExclusiveConnection&lt; PadLayer, Convolution2dLayer, pad_fold::FoldPadIntoConvolution2dImpl &gt; FoldPadIntoConvolution2d</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00233">FoldPadIntoLayer2d.hpp:233</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa7427025a851113a492de0b68b23d22a"><div class="ttname"><a href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a></div><div class="ttdeci">Optimizer::Optimizations MakeOptimizations(Args &amp;&amp;... args)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00043">Optimizer.hpp:43</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a227e9ab5e488aa90ba462790ba0e5aec"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a227e9ab5e488aa90ba462790ba0e5aec">armnn::optimizations::FoldPadIntoDepthwiseConvolution2d</a></div><div class="ttdeci">OptimizeForExclusiveConnection&lt; PadLayer, DepthwiseConvolution2dLayer, pad_fold::FoldPadIntoDepthwiseConvolution2dImpl &gt; FoldPadIntoDepthwiseConvolution2d</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00237">FoldPadIntoLayer2d.hpp:237</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00520">Descriptors.hpp:520</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="classarmnn_1_1_depthwise_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">armnn::DepthwiseConvolution2dLayer</a></div><div class="ttdoc">This layer represents a depthwise convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00015">DepthwiseConvolution2dLayer.hpp:15</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a></div><div class="ttdef"><b>Definition:</b> <a href="_add_broadcast_reshape_layer_8hpp_source.xhtml#l00015">AddBroadcastReshapeLayer.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a7563c5b899e7d0ada08fd0fdb202f205"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">armnn::Graph::AddLayer</a></div><div class="ttdeci">LayerT * AddLayer(Args &amp;&amp;... args)</div><div class="ttdoc">Adds a new layer, of type LayerType, to the graph constructed with the arguments passed. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00402">Graph.hpp:402</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#l00373">Descriptors.hpp:373</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a98b1109a9006f8cc7d4566146a3bd737"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">armnn::Graph::cbegin</a></div><div class="ttdeci">ConstIterator cbegin() const</div><div class="ttdoc">Returns const iterator pointing to the beginning of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00172">Graph.hpp:172</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00420">Descriptors.hpp:420</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_adcfb97035799ea4c043f9ef370714815"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">armnn::OutputSlot::Connect</a></div><div class="ttdeci">int Connect(InputSlot &amp;destination)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00083">Layer.cpp:83</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimizer_xhtml_a1f48ba622b76ea04d15c9b62f642bf08"><div class="ttname"><a href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a></div><div class="ttdeci">static void Pass(Graph &amp;graph, const Optimizations &amp;optimizations)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8cpp_source.xhtml#l00016">Optimizer.cpp:16</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#l00030">IRuntime.hpp:30</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&amp;#39;t count and are ignored. </div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling2dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00383">Descriptors.hpp:383</a></div></div>
<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml_a6266a703017d7296f87cc4923df2d725"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml#a6266a703017d7296f87cc4923df2d725">armnn::Convolution2dLayer::m_Weight</a></div><div class="ttdeci">std::shared_ptr&lt; ConstTensorHandle &gt; m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00020">Convolution2dLayer.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00369">Descriptors.hpp:369</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#l00360">Tensor.hpp:360</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__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="classarmnn_1_1_pad_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_pad_layer.xhtml">armnn::PadLayer</a></div><div class="ttdoc">This layer represents a pad operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pad_layer_8hpp_source.xhtml#l00014">PadLayer.hpp:14</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#l00377">Descriptors.hpp:377</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="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#l00375">Descriptors.hpp:375</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00993">Descriptors.hpp:993</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">armnn::Layer::GetInputSlot</a></div><div class="ttdeci">const InputSlot &amp; GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00316">Layer.hpp:316</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00458">Descriptors.hpp:458</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00510">Descriptors.hpp:510</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_output_layer_8hpp_source.xhtml#l00013">OutputLayer.hpp:13</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">armnn::Status::Success</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00367">Descriptors.hpp:367</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#l01613">Network.cpp:1613</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</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#l00024">IRuntime.hpp:24</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#l00361">Tensor.hpp:361</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#l00173">INetwork.hpp:173</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00460">Descriptors.hpp:460</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#l00029">Graph.hpp:29</a></div></div>
<div class="ttc" id="classarmnn_1_1_pooling2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_pooling2d_layer.xhtml">armnn::Pooling2dLayer</a></div><div class="ttdoc">This layer represents a pooling 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pooling2d_layer_8hpp_source.xhtml#l00013">Pooling2dLayer.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00385">Descriptors.hpp:385</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#l00101">IRuntime.hpp:101</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00363">Descriptors.hpp:363</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00512">Descriptors.hpp:512</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a></div><div class="ttdoc">The padding fields count, but are ignored. </div></div>
<div class="ttc" id="classarmnn_1_1_depthwise_convolution2d_layer_xhtml_a6266a703017d7296f87cc4923df2d725"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml#a6266a703017d7296f87cc4923df2d725">armnn::DepthwiseConvolution2dLayer::m_Weight</a></div><div class="ttdeci">std::shared_ptr&lt; ConstTensorHandle &gt; m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00019">DepthwiseConvolution2dLayer.hpp:19</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a279d0a7c56966cea334303d48a874964"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a279d0a7c56966cea334303d48a874964">armnn::optimizations::FoldPadIntoPooling2d</a></div><div class="ttdeci">OptimizeForExclusiveConnection&lt; PadLayer, Pooling2dLayer, pad_fold::FoldPadIntoPooling2dImpl &gt; FoldPadIntoPooling2d</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00239">FoldPadIntoLayer2d.hpp:239</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_input_layer_8hpp_source.xhtml#l00013">InputLayer.hpp:13</a></div></div>
<div class="ttc" id="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a></div><div class="ttdeci">bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_a7e5c5771d741dd5473989047a9314728"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">armnn::OutputSlot::SetTensorInfo</a></div><div class="ttdeci">void SetTensorInfo(const TensorInfo &amp;tensorInfo) override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00058">Layer.cpp:58</a></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_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot &amp; GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00318">Layer.hpp:318</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</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="classarmnn_1_1_graph_xhtml_a02fd29b6dc3e21fbe4484362d85893bc"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">armnn::Graph::cend</a></div><div class="ttdeci">ConstIterator cend() const</div><div class="ttdoc">Returns const iterator pointing to the end of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00174">Graph.hpp:174</a></div></div>
<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a></div><div class="ttdoc">This layer represents a convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00015">Convolution2dLayer.hpp:15</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr&lt; INetwork, void(*)(INetwork *network)&gt; INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00172">INetwork.hpp:172</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#l00329">Descriptors.hpp:329</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#l00530">Network.cpp:530</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#l00379">Descriptors.hpp:379</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00472">Descriptors.hpp:472</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#l00210">Layer.hpp:210</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
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