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| author | Nikhil Raj <nikhil.raj@arm.com> | 2022-08-19 15:23:36 +0100 |
|---|---|---|
| committer | Nikhil Raj <nikhil.raj@arm.com> | 2022-08-19 15:23:36 +0100 |
| commit | 7bfd38a721360183f3392f9ab35db18a0dd7fef8 (patch) | |
| tree | 5b4da2f2e88636c939afbafa2571170297114e40 /22.08/_fold_pad_tests_8cpp.xhtml | |
| parent | d5d43d82c0137e08553e44345c609cdd1a7931c7 (diff) | |
| download | armnn-7bfd38a721360183f3392f9ab35db18a0dd7fef8.tar.gz | |
Update Doxygen for 22.08 Release
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I4789fe868e0492839be1482e5cee3642ed90d756
Diffstat (limited to '22.08/_fold_pad_tests_8cpp.xhtml')
| -rw-r--r-- | 22.08/_fold_pad_tests_8cpp.xhtml | 230 |
1 files changed, 230 insertions, 0 deletions
diff --git a/22.08/_fold_pad_tests_8cpp.xhtml b/22.08/_fold_pad_tests_8cpp.xhtml new file mode 100644 index 0000000000..45a574ef33 --- /dev/null +++ b/22.08/_fold_pad_tests_8cpp.xhtml @@ -0,0 +1,230 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/armnn/test/optimizations/FoldPadTests.cpp File Reference</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" 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+<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">22.08</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_fold_pad_tests_8cpp.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="summary"> +<a href="#func-members">Functions</a> </div> + <div class="headertitle"> +<div class="title">FoldPadTests.cpp File Reference</div> </div> +</div><!--header--> +<div class="contents"> +<div class="textblock"><code>#include "<a class="el" href="_layers_fwd_8hpp_source.xhtml">LayersFwd.hpp</a>"</code><br /> +<code>#include <<a class="el" href="_network_8hpp_source.xhtml">Network.hpp</a>></code><br /> +<code>#include <TestUtils.hpp></code><br /> +<code>#include <doctest/doctest.h></code><br /> +<code>#include <<a class="el" href="include_2armnn_2backends_2_tensor_handle_8hpp_source.xhtml">armnn/backends/TensorHandle.hpp</a>></code><br /> +<code>#include <<a class="el" href="_optimizer_8hpp_source.xhtml">Optimizer.hpp</a>></code><br /> +</div> +<p><a href="_fold_pad_tests_8cpp_source.xhtml">Go to the source code of this file.</a></p> +<table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a> +Functions</h2></td></tr> +<tr class="memitem:a77a062dba8ec73047ae4e734519f5ef8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="_fold_pad_tests_8cpp.xhtml#a77a062dba8ec73047ae4e734519f5ef8">TEST_SUITE</a> ("Optimizer")</td></tr> +<tr class="separator:a77a062dba8ec73047ae4e734519f5ef8"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<h2 class="groupheader">Function Documentation</h2> +<a id="a77a062dba8ec73047ae4e734519f5ef8"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a77a062dba8ec73047ae4e734519f5ef8">◆ </a></span>TEST_SUITE()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">TEST_SUITE </td> + <td>(</td> + <td class="paramtype">"Optimizer" </td> + <td class="paramname"></td><td>)</td> + <td></td> + </tr> + </table> +</div><div class="memdoc"> + +<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#l00456">Graph::AddLayer()</a>, <a class="el" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::Average</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00179">Graph::cbegin()</a>, <a class="el" href="_graph_8hpp_source.xhtml#l00181">Graph::cend()</a>, <a class="el" href="est_utils_2_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#l00112">OutputSlot::Connect()</a>, <a class="el" href="_network_8cpp_source.xhtml#l00475">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#l00324">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#l00326">Layer::GetOutputSlot()</a>, <a class="el" href="_layer_with_parameters_8hpp_source.xhtml#l00019">LayerWithParameters< Parameters >::GetParameters()</a>, <a class="el" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::IgnoreValue</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00543">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00669">DepthwiseConvolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00388">Pooling2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00545">Convolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00671">DepthwiseConvolution2dDescriptor::m_DataLayout</a>, <a class="el" href="_constant_layer_8hpp_source.xhtml#l00044">ConstantLayer::m_LayerOutput</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00374">Pooling2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00386">Pooling2dDescriptor::m_PaddingMethod</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00368">Pooling2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00370">Pooling2dDescriptor::m_PadRight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00372">Pooling2dDescriptor::m_PadTop</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l01198">PadDescriptor::m_PadValue</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00378">Pooling2dDescriptor::m_PoolHeight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00366">Pooling2dDescriptor::m_PoolType</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00376">Pooling2dDescriptor::m_PoolWidth</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00380">Pooling2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00535">Convolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00661">DepthwiseConvolution2dDescriptor::m_StrideX</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00382">Pooling2dDescriptor::m_StrideY</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00537">Convolution2dDescriptor::m_StrideY</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00663">DepthwiseConvolution2dDescriptor::m_StrideY</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="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</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#l00087">OutputSlot::SetTensorInfo()</a>.</p> +<div class="fragment"><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> {</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <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> <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> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoConvolution2dLayer"</span>)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <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>  <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>  <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>  <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>  <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> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <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>  <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>  <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>, 1.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <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="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <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><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  input-><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="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  {2, 2},</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  {2, 2},</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  {0, 0}});</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <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><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  padLayer-><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="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  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="l00044"></a><span class="lineno"> 44</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  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="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  std::vector<float> weightsVector(18);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsVector);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="classarmnn_1_1_constant_layer.xhtml">ConstantLayer</a>* weightsLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_constant_layer.xhtml">ConstantLayer</a>>(<span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  weightsLayer-><a class="code" href="classarmnn_1_1_constant_layer.xhtml#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a> = std::make_shared<ScopedTensorHandle>(weights);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  weightsLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(weightsInfo);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <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><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>>(convolution2dDescriptor, <span class="stringliteral">"conv2d"</span>);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  conv2dLayer-><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="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <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><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="comment">// Connect up layers - input -> pad -> conv2d -> output</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  input-><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->GetInputSlot(0));</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  padLayer->GetOutputSlot().Connect(conv2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  weightsLayer-><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>(conv2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1));</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  conv2dLayer-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <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)-><span class="keywordtype">bool</span> {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayerParams = conv2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>();</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">return</span> IsLayerOfType<Convolution2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"conv2d"</span>) &&</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  (conv2dLayerParams.m_PadLeft == 0) && (conv2dLayerParams.m_PadRight == 0) &&</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  (conv2dLayerParams.m_PadTop == 0) && (conv2dLayerParams.m_PadBottom == 0) &&</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  (conv2dLayerParams.m_StrideX == 1) && (conv2dLayerParams.m_StrideY == 1) &&</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  (conv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) && (conv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  };</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  CHECK(<a class="code" href="est_utils_2_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>(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  &IsLayerOfType<ConstantLayer>,</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  checkSimpleConv2d,</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <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="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <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)-><span class="keywordtype">bool</span> {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayerParams = conv2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>();</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">return</span> IsLayerOfType<Convolution2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"folded-pad-into-conv2d"</span>) &&</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  (conv2dLayerParams.m_PadLeft == 2) && (conv2dLayerParams.m_PadRight == 2) &&</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  (conv2dLayerParams.m_PadTop == 2) && (conv2dLayerParams.m_PadBottom == 2) &&</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  (conv2dLayerParams.m_StrideX == 1) && (conv2dLayerParams.m_StrideY == 1) &&</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  (conv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) && (conv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  };</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  CHECK(<a class="code" href="est_utils_2_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>(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  &IsLayerOfType<ConstantLayer>,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  checkPadFoldedIntoConv2d,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoDepthwiseConvolution2dLayer"</span>)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <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="l00104"></a><span class="lineno"> 104</span>  <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="l00105"></a><span class="lineno"> 105</span>  <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="l00106"></a><span class="lineno"> 106</span>  <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="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <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="l00109"></a><span class="lineno"> 109</span>  <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="l00110"></a><span class="lineno"> 110</span>  <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>, 1.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <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="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <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><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  input-><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="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  {2, 2},</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  {2, 2},</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  {0, 0}});</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <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><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  padLayer-><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="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> depthwiseConvolution2dDescriptor;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  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="l00126"></a><span class="lineno"> 126</span>  depthwiseConvolution2dDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  depthwiseConvolution2dDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  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="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  std::vector<float> weightsVector(18);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsVector);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">auto</span>* weightsLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_constant_layer.xhtml">ConstantLayer</a>>(<span class="stringliteral">"weights"</span>);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  weightsLayer-><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>(weightsInfo);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  weightsLayer-><a class="code" href="classarmnn_1_1_constant_layer.xhtml#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a> = std::make_shared<ScopedTensorHandle>(weights);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">auto</span>* depthwiseConv2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>>(depthwiseConvolution2dDescriptor,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="stringliteral">"depthwiseConv2d"</span>);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  depthwiseConv2dLayer-><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="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <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><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="comment">// Connect up layers - input -> pad -> depthwiseConv2d -> output</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  input-><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->GetInputSlot(0));</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  padLayer->GetOutputSlot().Connect(depthwiseConv2dLayer->GetInputSlot(0));</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  weightsLayer-><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>(depthwiseConv2dLayer->GetInputSlot(1));</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  depthwiseConv2dLayer->GetOutputSlot().Connect(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <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)-><span class="keywordtype">bool</span> {</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayerParams = depthwiseConv2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>();</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">return</span> IsLayerOfType<DepthwiseConvolution2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"depthwiseConv2d"</span>) &&</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  (depthwiseConv2dLayerParams.m_PadLeft == 0) && (depthwiseConv2dLayerParams.m_PadRight == 0) &&</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  (depthwiseConv2dLayerParams.m_PadTop == 0) && (depthwiseConv2dLayerParams.m_PadBottom == 0) &&</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  (depthwiseConv2dLayerParams.m_StrideX == 1) && (depthwiseConv2dLayerParams.m_StrideY == 1) &&</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  (depthwiseConv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &&</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  (depthwiseConv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  };</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  CHECK(<a class="code" href="est_utils_2_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>(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  &IsLayerOfType<ConstantLayer>,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  checkSimpleDepthwiseConv2d,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="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="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <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)-><span class="keywordtype">bool</span> {</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> <span class="keyword">auto</span> depthwiseConv2dLayerParams = depthwiseConv2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>();</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">return</span> IsLayerOfType<DepthwiseConvolution2dLayer>(layer) &&</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  (layer->GetNameStr() == <span class="stringliteral">"folded-pad-into-depthwiseConv2d"</span>) &&</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  (depthwiseConv2dLayerParams.m_PadLeft == 2) && (depthwiseConv2dLayerParams.m_PadRight == 2) &&</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  (depthwiseConv2dLayerParams.m_PadTop == 2) && (depthwiseConv2dLayerParams.m_PadBottom == 2) &&</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  (depthwiseConv2dLayerParams.m_StrideX == 1) && (depthwiseConv2dLayerParams.m_StrideY == 1) &&</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  (depthwiseConv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &&</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  (depthwiseConv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  };</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  CHECK(<a class="code" href="est_utils_2_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>(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  &IsLayerOfType<ConstantLayer>,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  checkPadFoldedIntoDepthwiseConv2d,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> }</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoPooling2dLayer"</span>)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <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="l00190"></a><span class="lineno"> 190</span>  <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="l00191"></a><span class="lineno"> 191</span>  <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="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <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="l00194"></a><span class="lineno"> 194</span>  <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="l00195"></a><span class="lineno"> 195</span>  <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="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <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><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  input-><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="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {1, 1},</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  {1, 1},</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  {0, 0}});</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <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><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  padLayer-><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="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  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="l00210"></a><span class="lineno"> 210</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  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="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <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><<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>>(pooling2dDescriptor, <span class="stringliteral">"pool2d"</span>);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  pool2dLayer-><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="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <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><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">// Connect up layers - input -> pad -> pool2d -> output</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  input-><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->GetInputSlot(0));</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  padLayer->GetOutputSlot().Connect(pool2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  pool2dLayer-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">auto</span> checkSimplePool2d = [&](<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="l00227"></a><span class="lineno"> 227</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">return</span> IsLayerOfType<Pooling2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"pool2d"</span>) &&</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  (pool2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>() == pooling2dDescriptor);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  };</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  CHECK(<a class="code" href="est_utils_2_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="l00233"></a><span class="lineno"> 233</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  checkSimplePool2d,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <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="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keyword">auto</span> checkPadFoldedIntoPool2d = [&](<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="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">if</span> (!IsLayerOfType<Pooling2dLayer>(layer) || (layer->GetNameStr() != <span class="stringliteral">"folded-pad-into-pool2d"</span>))</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  }</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> </div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pool2dLayerParams = pool2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>();</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pool2dLayerParamsNoPad = pool2dLayerParams;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  pool2dLayerParamsNoPad.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <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="l00255"></a><span class="lineno"> 255</span>  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="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordflow">return</span> (pool2dLayerParamsNoPad == pooling2dDescriptor) && (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> == 1) &&</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> == 1) && (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> == 1) &&</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  (pool2dLayerParams.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> == 1) && (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="l00260"></a><span class="lineno"> 260</span>  };</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  CHECK(<a class="code" href="est_utils_2_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="l00263"></a><span class="lineno"> 263</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  checkPadFoldedIntoPool2d,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoPooling2d_PadWithMultipleOutputsShouldNotBeOptimized"</span>)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="comment">// In this test case we'll setup a pad layer with two outputs. One goes to a polling layers and the other</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="comment">// goes to an output layer. FoldPadLayerIntoPooling2d should not optimize this graph as it uses the</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="comment">// OptimizeForExclusiveConnection method.</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <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="l00275"></a><span class="lineno"> 275</span>  <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="l00276"></a><span class="lineno"> 276</span>  <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="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <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="l00279"></a><span class="lineno"> 279</span>  <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="l00280"></a><span class="lineno"> 280</span>  <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="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <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><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  input-><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="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  {1, 1},</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  {1, 1},</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  {0, 0}});</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <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><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  padLayer-><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="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  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="l00295"></a><span class="lineno"> 295</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  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="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <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><<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>>(pooling2dDescriptor, <span class="stringliteral">"pool2d"</span>);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  pool2dLayer-><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="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <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><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="comment">// Connect up layers - input -> pad -> pool2d -> output</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  input-><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->GetInputSlot(0));</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  padLayer->GetOutputSlot().Connect(pool2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  pool2dLayer-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="comment">// Add the alternative branch from the pas layer to an output layer.</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* secondOutput = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(1, <span class="stringliteral">"dummy output"</span>);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  padLayer-><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>(secondOutput-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keyword">auto</span> checkSimplePool2d = [&](<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="l00316"></a><span class="lineno"> 316</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">return</span> IsLayerOfType<Pooling2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"pool2d"</span>) &&</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  (pool2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>() == pooling2dDescriptor);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  };</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">// Initial sequence.</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  CHECK(<a class="code" href="est_utils_2_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>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  checkSimplePool2d,</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  &IsLayerOfType<OutputLayer>,</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <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="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// The network should not change.</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  CHECK(<a class="code" href="est_utils_2_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="l00333"></a><span class="lineno"> 333</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  checkSimplePool2d,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  &IsLayerOfType<OutputLayer>,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> }</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoPooling2dLayer_PoolingLayerWithExcludePaddingShouldNotTakeMorePadding"</span>)</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> {</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <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="l00343"></a><span class="lineno"> 343</span>  <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="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <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="l00346"></a><span class="lineno"> 346</span>  <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="l00347"></a><span class="lineno"> 347</span>  <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="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <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="l00350"></a><span class="lineno"> 350</span>  <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="l00351"></a><span class="lineno"> 351</span>  <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="l00352"></a><span class="lineno"> 352</span> </div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <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><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  input-><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="l00355"></a><span class="lineno"> 355</span> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  {1, 1},</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  {1, 1},</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  {0, 0}});</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <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><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  padLayer-><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="l00363"></a><span class="lineno"> 363</span> </div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  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="l00366"></a><span class="lineno"> 366</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  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="l00371"></a><span class="lineno"> 371</span>  <span class="comment">// Include a pad with the pooling layer. This should prevent the optimization working.</span></div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  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="l00377"></a><span class="lineno"> 377</span> </div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <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><<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>>(pooling2dDescriptor, <span class="stringliteral">"pool2d"</span>);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  pool2dLayer-><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="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <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><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="comment">// Connect up layers - input -> pad -> pool2d -> output</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  input-><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->GetInputSlot(0));</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  padLayer->GetOutputSlot().Connect(pool2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  pool2dLayer-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keyword">auto</span> checkSimplePool2d = [&](<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="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">return</span> IsLayerOfType<Pooling2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"pool2d"</span>) &&</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  (pool2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>() == pooling2dDescriptor);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  };</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> </div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  CHECK(<a class="code" href="est_utils_2_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="l00395"></a><span class="lineno"> 395</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  checkSimplePool2d,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <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="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="comment">// The optimization should not have modified the graph.</span></div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  CHECK(<a class="code" href="est_utils_2_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="l00404"></a><span class="lineno"> 404</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  checkSimplePool2d,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> }</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoPooling2dLayer_MaxPoolingLayerWithLargePadValueShouldNotBeFolded"</span>)</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <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="l00413"></a><span class="lineno"> 413</span>  <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="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <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="l00416"></a><span class="lineno"> 416</span>  <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="l00417"></a><span class="lineno"> 417</span>  <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="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <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="l00420"></a><span class="lineno"> 420</span>  <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="l00421"></a><span class="lineno"> 421</span>  <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="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <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><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  input-><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="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  {1, 1},</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  {1, 1},</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  {0, 0}});</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <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="l00431"></a><span class="lineno"> 431</span>  <span class="comment">// Set this to -std::numeric_limits<float>::infinity() to make the fold happen.</span></div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  padDescriptor.m_PadValue = 0;</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <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><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  padLayer-><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="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  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="l00439"></a><span class="lineno"> 439</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  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="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <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><<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>>(pooling2dDescriptor, <span class="stringliteral">"pool2d"</span>);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  pool2dLayer-><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="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <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><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span> </div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="comment">// Connect up layers - input -> pad -> pool2d -> output</span></div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  input-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  padLayer-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  pool2dLayer-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keyword">auto</span> checkSimplePool2d = [&](<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="l00456"></a><span class="lineno"> 456</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordflow">return</span> IsLayerOfType<Pooling2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"pool2d"</span>) &&</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  (pool2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>() == pooling2dDescriptor);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  };</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  CHECK(<a class="code" href="est_utils_2_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="l00462"></a><span class="lineno"> 462</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  checkSimplePool2d,</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> </div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <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="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="comment">// The optimization should not have modified the graph.</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  CHECK(<a class="code" href="est_utils_2_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="l00471"></a><span class="lineno"> 471</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  checkSimplePool2d,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> }</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoPooling2dLayer_QuantizedAveragePoolingShouldNotBeFolded"</span>)</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> {</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <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="l00481"></a><span class="lineno"> 481</span>  <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="l00482"></a><span class="lineno"> 482</span>  <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="l00483"></a><span class="lineno"> 483</span> </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <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><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  input-><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="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  {1, 1},</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  {1, 1},</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  {0, 0}});</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <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><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  padLayer-><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="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  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="l00501"></a><span class="lineno"> 501</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  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="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <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><<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>>(pooling2dDescriptor, <span class="stringliteral">"pool2d"</span>);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  pool2dLayer-><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="l00509"></a><span class="lineno"> 509</span> </div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <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><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span> </div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="comment">// Connect up layers - input -> pad -> pool2d -> output</span></div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  input-><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->GetInputSlot(0));</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  padLayer->GetOutputSlot().Connect(pool2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  pool2dLayer-><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-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keyword">auto</span> checkSimplePool2d = [&](<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="l00518"></a><span class="lineno"> 518</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pool2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="keywordflow">return</span> IsLayerOfType<Pooling2dLayer>(layer) && (layer->GetNameStr() == <span class="stringliteral">"pool2d"</span>) &&</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  (pool2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>() == pooling2dDescriptor);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  };</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  CHECK(<a class="code" href="est_utils_2_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="l00524"></a><span class="lineno"> 524</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  checkSimplePool2d,</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> </div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <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="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="comment">// The optimization should not have modified the graph.</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  CHECK(<a class="code" href="est_utils_2_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="l00533"></a><span class="lineno"> 533</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  &IsLayerOfType<PadLayer>,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  checkSimplePool2d,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> }</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> </div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> <span class="preprocessor">#if defined(ARMNNREF_ENABLED)</span></div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoPooling2dLayer_ExecuteInferenceWithAndWithoutOptimization"</span>)</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> {</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="comment">// The idea of this test to run a simple pad+pool2d network twice. Once</span></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="comment">// with FoldPadLayerIntoPooling2dLayer enabled and a second time with it</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">// avoided. The output tensors of each should match.</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <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="l00546"></a><span class="lineno"> 546</span>  <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="l00547"></a><span class="lineno"> 547</span>  <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="l00548"></a><span class="lineno"> 548</span>  std::vector<float> inputData({2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  18.0f, 20.0f, 24.0f, 22.0f,</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  20.0f, 18.0f, 22.0f, 24.0f,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  });</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  {</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="comment">// Create a network of input, pad, pooling 2D, output.</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <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="l00562"></a><span class="lineno"> 562</span> </div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <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="l00565"></a><span class="lineno"> 565</span>  inputLayer-><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="l00566"></a><span class="lineno"> 566</span> </div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  {1, 1},</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  {1, 1},</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  {0, 0}});</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* padLayer = network->AddPadLayer(padDescriptor, <span class="stringliteral">"Pad"</span>);</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <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="l00573"></a><span class="lineno"> 573</span>  padLayer->GetOutputSlot(0).SetTensorInfo(paddedInfo);</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  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="l00577"></a><span class="lineno"> 577</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  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="l00582"></a><span class="lineno"> 582</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pool2dLayer = network->AddPooling2dLayer(pooling2dDescriptor, <span class="stringliteral">"Pool2D"</span>);</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <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="l00584"></a><span class="lineno"> 584</span>  pool2dLayer-><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="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> </div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="comment">// Connect layers</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  inputLayer-><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->GetInputSlot(0));</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  padLayer->GetOutputSlot(0).Connect(pool2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  pool2dLayer-><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-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <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="l00595"></a><span class="lineno"> 595</span>  <span class="comment">// Optimise the network</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <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->GetDeviceSpec());</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors{{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(inputTensorInfo, inputData.data())}};</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <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="l00606"></a><span class="lineno"> 606</span>  std::vector<float> optimizedData(32, -std::numeric_limits<float>::infinity());</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <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="l00608"></a><span class="lineno"> 608</span>  <span class="comment">// Execute network</span></div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="comment">// Unload it.</span></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  run->UnloadNetwork(networkIdentifier);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> </div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <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="l00614"></a><span class="lineno"> 614</span>  <span class="comment">// a second output layer. This will prevent the FoldPadLayerIntoPooling2dLayer optimization from working.</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="comment">// A previous test, FoldPadLayerIntoPooling2d_PadWithMultipleOutputsShouldNotBeOptimized, has proved that doing</span></div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="comment">// this will avoid the optimization.</span></div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* dummyOutputLayer = network->AddOutputLayer(1);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  padLayer-><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-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="comment">// Optimize and load and execute it a second time.</span></div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run->GetDeviceSpec());</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  std::vector<float> goldenData(32, 0.0f);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  std::vector<float> padOutputData(72, 0.0f);</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <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="l00626"></a><span class="lineno"> 626</span>  {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(paddedInfo, padOutputData.data())}};</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  run->EnqueueWorkload(networkIdentifier, inputTensors, goldenTensors);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> </div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="comment">// Now we can compare goldenData against optimizedData. They should be the same.</span></div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  CHECK(std::equal(goldenData.begin(), goldenData.end(), optimizedData.begin()));</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  }</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception& e)</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  {</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  std::cerr << e.what() << std::endl;</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <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="l00636"></a><span class="lineno"> 636</span>  }</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> }</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span> </div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimization"</span>)</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> {</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="comment">// The idea of this test to run a simple pad+conv2d network twice. Once</span></div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="comment">// with FoldPadLayerIntoConv2dLayer enabled and a second time with it</span></div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="comment">// avoided. The output tensors of each should match.</span></div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <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="l00645"></a><span class="lineno"> 645</span>  <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="l00646"></a><span class="lineno"> 646</span>  <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="l00647"></a><span class="lineno"> 647</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 5, 5, 4}; <span class="comment">// NHWCout</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span> </div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  std::vector<float> inputData({2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> </div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  18.0f, 20.0f, 24.0f, 22.0f,</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  20.0f, 18.0f, 22.0f, 24.0f,</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> </div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  });</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  {</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="comment">// Create a network of input, pad, pooling 2D, output.</span></div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <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="l00668"></a><span class="lineno"> 668</span> </div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <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="l00671"></a><span class="lineno"> 671</span>  inputLayer-><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="l00672"></a><span class="lineno"> 672</span> </div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  {1, 1},</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  {1, 1},</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  {0, 0}});</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* padLayer = network->AddPadLayer(padDescriptor, <span class="stringliteral">"Pad"</span>);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <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="l00679"></a><span class="lineno"> 679</span>  padLayer->GetOutputSlot(0).SetTensorInfo(paddedInfo);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span> </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convDescriptor;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  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="l00683"></a><span class="lineno"> 683</span>  convDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  convDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  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="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  std::vector<float> weightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <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>, 1.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  std::vector<float> biasVector = {5, 6, 7, 8};</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, 1.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> bias(biasInfo, biasVector);</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> </div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* conv2dLayer = network->AddConvolution2dLayer(convDescriptor, <span class="stringliteral">"Conv2D"</span>);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span> </div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <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="l00700"></a><span class="lineno"> 700</span>  conv2dLayer-><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="l00701"></a><span class="lineno"> 701</span> </div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="comment">// Connect layers</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  inputLayer-><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->GetInputSlot(0));</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  padLayer->GetOutputSlot(0).Connect(conv2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  conv2dLayer-><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-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> </div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <span class="keyword">auto</span> weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  weightsLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(weights.GetInfo());</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  weightsLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(conv2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> </div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">auto</span> biasLayer = network->AddConstantLayer(bias, <span class="stringliteral">"Bias"</span>);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  biasLayer->GetOutputSlot(0).SetTensorInfo(bias.GetInfo());</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  biasLayer->GetOutputSlot(0).Connect(conv2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span> </div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  <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="l00719"></a><span class="lineno"> 719</span>  <span class="comment">// Optimise the network</span></div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <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->GetDeviceSpec());</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> </div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors{{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(inputTensorInfo, inputData.data())}};</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> </div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <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="l00730"></a><span class="lineno"> 730</span>  std::vector<float> optimizedData(100, -std::numeric_limits<float>::infinity());</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <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="l00732"></a><span class="lineno"> 732</span>  <span class="comment">// Execute network</span></div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="comment">// Unload it.</span></div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  run->UnloadNetwork(networkIdentifier);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> </div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <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="l00738"></a><span class="lineno"> 738</span>  <span class="comment">// a second output layer. This will prevent the FoldPadLayerIntoConv2dLayer optimization from working.</span></div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="comment">// A previous test, FoldPadLayerIntoConv2d_PadWithMultipleOutputsShouldNotBeOptimized, has proved that doing</span></div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="comment">// this will avoid the optimization.</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* dummyOutputLayer = network->AddOutputLayer(1);</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  padLayer-><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-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> </div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="comment">// Optimize and load and execute it a second time.</span></div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run->GetDeviceSpec());</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  std::vector<float> goldenData(100, 0.0f);</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  std::vector<float> padOutputData(108, 0.0f);</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <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="l00750"></a><span class="lineno"> 750</span>  {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(paddedInfo, padOutputData.data())}};</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  run->EnqueueWorkload(networkIdentifier, inputTensors, goldenTensors);</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> </div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="comment">// Now we can compare goldenData against optimizedData. They should be the same.</span></div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  CHECK(std::equal(goldenData.begin(), goldenData.end(), optimizedData.begin()));</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  }</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception& e)</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  {</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  std::cerr << e.what() << std::endl;</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <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="l00760"></a><span class="lineno"> 760</span>  }</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span> }</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> </div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> TEST_CASE(<span class="stringliteral">"FoldPadLayerIntoDepthwiseConv2dLayer_ExecuteInferenceWithAndWithoutOptimization"</span>)</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span> {</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="comment">// The idea of this test to run a simple pad+depthwiseconv2d network twice. Once</span></div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <span class="comment">// with FoldPadLayerIntoDeptwiseConv2dLayer enabled and a second time with it</span></div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <span class="comment">// avoided. The output tensors of each should match.</span></div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <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="l00769"></a><span class="lineno"> 769</span>  <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="l00770"></a><span class="lineno"> 770</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = {1, 2, 2, 12}; <span class="comment">// 1HWCout</span></div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 5, 5, 12}; <span class="comment">// NHWCout</span></div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  std::vector<float> inputData({2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span> </div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  18.0f, 20.0f, 24.0f, 22.0f,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  20.0f, 18.0f, 22.0f, 24.0f,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> </div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  });</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  {</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="comment">// Create a network of input, pad, pooling 2D, output.</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <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="l00792"></a><span class="lineno"> 792</span> </div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <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="l00795"></a><span class="lineno"> 795</span>  inputLayer-><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="l00796"></a><span class="lineno"> 796</span> </div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{0, 0},</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  {1, 1},</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  {1, 1},</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  {0, 0}});</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* padLayer = network->AddPadLayer(padDescriptor, <span class="stringliteral">"Pad"</span>);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <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="l00803"></a><span class="lineno"> 803</span>  padLayer->GetOutputSlot(0).SetTensorInfo(paddedInfo);</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span> </div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> convDescriptor;</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  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="l00807"></a><span class="lineno"> 807</span>  convDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  convDescriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  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="l00810"></a><span class="lineno"> 810</span> </div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  std::vector<float> weightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <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>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  std::vector<float> biasVector = {5, 6, 7, 8, 9, 10, 11, 12, 5, 6, 7, 8};</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({12}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> bias(biasInfo, biasVector);</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span> </div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* conv2dLayer = network->AddDepthwiseConvolution2dLayer(convDescriptor,</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="stringliteral">"DepthwiseConv2D"</span>);</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> </div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <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="l00825"></a><span class="lineno"> 825</span>  conv2dLayer-><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="l00826"></a><span class="lineno"> 826</span> </div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="comment">// Connect layers</span></div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  inputLayer-><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->GetInputSlot(0));</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  padLayer->GetOutputSlot(0).Connect(conv2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  conv2dLayer-><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-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span> </div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keyword">auto</span> weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  weightsLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(weights.GetInfo());</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  weightsLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(conv2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> </div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="keyword">auto</span> biasLayer = network->AddConstantLayer(bias, <span class="stringliteral">"Bias"</span>);</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  biasLayer->GetOutputSlot(0).SetTensorInfo(bias.GetInfo());</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  biasLayer->GetOutputSlot(0).Connect(conv2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> </div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <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="l00844"></a><span class="lineno"> 844</span>  <span class="comment">// Optimise the network</span></div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <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->GetDeviceSpec());</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> </div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors{{0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(inputTensorInfo, inputData.data())}};</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> </div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <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="l00855"></a><span class="lineno"> 855</span>  std::vector<float> optimizedData(300, -std::numeric_limits<float>::infinity());</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <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="l00857"></a><span class="lineno"> 857</span>  <span class="comment">// Execute network</span></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <span class="comment">// Unload it.</span></div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  run->UnloadNetwork(networkIdentifier);</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> </div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <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="l00863"></a><span class="lineno"> 863</span>  <span class="comment">// a second output layer. This will prevent the FoldPadLayerIntoDepthwiseConv2dLayer optimization from working.</span></div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="comment">// A previous test, FoldPadLayerIntoDepthwiseConv2d_PadWithMultipleOutputsShouldNotBeOptimized, has proved that</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="comment">// doing this will avoid the optimization.</span></div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* dummyOutputLayer = network->AddOutputLayer(1);</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  padLayer-><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-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span> </div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <span class="comment">// Optimize and load and execute it a second time.</span></div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>}, run->GetDeviceSpec());</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  std::vector<float> goldenData(300, 0.0f);</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  std::vector<float> padOutputData(108, 0.0f);</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <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="l00875"></a><span class="lineno"> 875</span>  {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(paddedInfo, padOutputData.data())}};</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  run->EnqueueWorkload(networkIdentifier, inputTensors, goldenTensors);</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> </div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="comment">// Now we can compare goldenData against optimizedData. They should be the same.</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  CHECK(std::equal(goldenData.begin(), goldenData.end(), optimizedData.begin()));</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  }</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception& e)</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  {</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  std::cerr << e.what() << std::endl;</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <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="l00885"></a><span class="lineno"> 885</span>  }</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> }</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span> }</div><div class="ttc" id="classarmnn_1_1_constant_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_constant_layer.xhtml">armnn::ConstantLayer</a></div><div class="ttdoc">A layer that the constant data can be bound to. </div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.xhtml#l00015">ConstantLayer.hpp:15</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::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#l00543">Descriptors.hpp:543</a></div></div> +<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#l00545">Descriptors.hpp:545</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 &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00049">Runtime.cpp:49</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#l00068">INetwork.hpp:68</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#l00374">Descriptors.hpp:374</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#l00669">Descriptors.hpp:669</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#l00368">Descriptors.hpp:368</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< PadLayer, Convolution2dLayer, pad_fold::FoldPadIntoConvolution2dImpl > FoldPadIntoConvolution2d</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00271">FoldPadIntoLayer2d.hpp:271</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 &&... 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="est_utils_2_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="est_utils_2_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="est_utils_2_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</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< PadLayer, DepthwiseConvolution2dLayer, pad_fold::FoldPadIntoDepthwiseConvolution2dImpl > FoldPadIntoDepthwiseConvolution2d</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00275">FoldPadIntoLayer2d.hpp:275</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#l00671">Descriptors.hpp:671</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_constant_layer_xhtml_ad0c4b8ee0efd8f9336571cbeab8a53fe"><div class="ttname"><a href="classarmnn_1_1_constant_layer.xhtml#ad0c4b8ee0efd8f9336571cbeab8a53fe">armnn::ConstantLayer::m_LayerOutput</a></div><div class="ttdeci">std::shared_ptr< ConstTensorHandle > m_LayerOutput</div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.xhtml#l00044">ConstantLayer.hpp:44</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 &&... 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#l00456">Graph.hpp:456</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00376">Descriptors.hpp:376</a></div></div> +<div class="ttc" id="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#l00179">Graph.hpp:179</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#l00495">Descriptors.hpp:495</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 &destination)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00112">Layer.cpp:112</a></div></div> +<div class="ttc" id="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 &graph, const Optimizations &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< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00033">IRuntime.hpp:33</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&#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#l00386">Descriptors.hpp:386</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#l00372">Descriptors.hpp:372</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00392">Tensor.hpp:392</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_with_parameters_xhtml_afa3e8a8f23589b1eaddbe203825bbdcf"><div class="ttname"><a href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">armnn::LayerWithParameters::GetParameters</a></div><div class="ttdeci">const Parameters & GetParameters() const override</div><div class="ttdoc">If the layer has a descriptor return it. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_with_parameters_8hpp_source.xhtml#l00019">LayerWithParameters.hpp:19</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#l00380">Descriptors.hpp:380</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &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#l00378">Descriptors.hpp:378</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#l01173">Descriptors.hpp:1173</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 & GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00324">Layer.hpp:324</a></div></div> +<div class="ttc" id="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#l00535">Descriptors.hpp:535</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#l00661">Descriptors.hpp:661</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#l00370">Descriptors.hpp:370</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01864">Network.cpp:1864</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_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a0d8160388a127c1a23b37bc88dc6e2ec"><div class="ttname"><a href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00027">IRuntime.hpp:27</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00393">Tensor.hpp:393</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00239">INetwork.hpp:239</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#l00537">Descriptors.hpp:537</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00030">Graph.hpp:30</a></div></div> +<div class="ttc" id="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#l00388">Descriptors.hpp:388</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00077">IRuntime.hpp:77</a></div></div> +<div class="ttc" id="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#l00366">Descriptors.hpp:366</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#l00663">Descriptors.hpp:663</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="namespacearmnn_1_1optimizations_xhtml_a279d0a7c56966cea334303d48a874964"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a279d0a7c56966cea334303d48a874964">armnn::optimizations::FoldPadIntoPooling2d</a></div><div class="ttdeci">OptimizeForExclusiveConnection< PadLayer, Pooling2dLayer, pad_fold::FoldPadIntoPooling2dImpl > FoldPadIntoPooling2d</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00277">FoldPadIntoLayer2d.hpp:277</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="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 &tensorInfo) override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00087">Layer.cpp:87</a></div></div> +<div class="ttc" id="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 & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00514">Tensor.cpp:514</a></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 & GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00326">Layer.hpp:326</a></div></div> +<div class="ttc" id="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 & 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#l00181">Graph.hpp:181</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< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00238">INetwork.hpp:238</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 &destination)=0</div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00332">Descriptors.hpp:332</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00475">Network.cpp:475</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00382">Descriptors.hpp:382</a></div></div> +<div class="ttc" id="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#l00620">Descriptors.hpp:620</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#l00217">Layer.hpp:217</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> +</div><!-- fragment --> +</div> +</div> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9d86fd1fbecbedf5bdb69c7e7235fe5f.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_f1cd0e6da811a659c139424442adfb5f.xhtml">optimizations</a></li><li class="navelem"><a class="el" href="_fold_pad_tests_8cpp.xhtml">FoldPadTests.cpp</a></li> + <li class="footer">Generated on Fri Aug 19 2022 14:38:31 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |