diff options
| author | Matthew Sloyan <matthew.sloyan@arm.com> | 2021-08-24 16:27:15 +0100 |
|---|---|---|
| committer | Matthew Sloyan <matthew.sloyan@arm.com> | 2021-08-24 16:27:40 +0100 |
| commit | f86be93b7492b381370cae7bf71eca8572a0cbae (patch) | |
| tree | 2a16d9b1892db2305851b2d91850f1c1635390b0 /21.08/_fully_connected_end_to_end_test_impl_8hpp_source.xhtml | |
| parent | ff4682943c0a64acb22643aac7793ad2ec2a1194 (diff) | |
| download | armnn-f86be93b7492b381370cae7bf71eca8572a0cbae.tar.gz | |
IVGCVSW-5924 Update 21.08 Doxygen Documents
* Also updated latest symlink.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: If9b4e0e52464abdf797b9eb858ae19bcc64c2aea
Diffstat (limited to '21.08/_fully_connected_end_to_end_test_impl_8hpp_source.xhtml')
| -rw-r--r-- | 21.08/_fully_connected_end_to_end_test_impl_8hpp_source.xhtml | 132 |
1 files changed, 132 insertions, 0 deletions
diff --git a/21.08/_fully_connected_end_to_end_test_impl_8hpp_source.xhtml b/21.08/_fully_connected_end_to_end_test_impl_8hpp_source.xhtml new file mode 100644 index 0000000000..faf78b7174 --- /dev/null +++ b/21.08/_fully_connected_end_to_end_test_impl_8hpp_source.xhtml @@ -0,0 +1,132 @@ +<!-- 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/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">21.08</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(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('_fully_connected_end_to_end_test_impl_8hpp_source.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="headertitle"> +<div class="title">FullyConnectedEndToEndTestImpl.hpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_fully_connected_end_to_end_test_impl_8hpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>></span></div><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="preprocessor">#include <doctest/doctest.h></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> {</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateFullyConnectedNetworkNonConstWeights(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& weightsTensorInfo,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor)</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* inputLayer = network->AddInputLayer(0, <span class="stringliteral">"Input"</span>);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* weightsInputLayer = network->AddInputLayer(1, <span class="stringliteral">"Weights_Input"</span>);</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, <span class="stringliteral">"Fully_Connected"</span>);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0, <span class="stringliteral">"Output"</span>);</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="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(weightsInputLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> }</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateFullyConnectedNetworkNonConstWeightsConstBias(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& weightsTensorInfo,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& biasTensorInfo,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& biasConstantTensor,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor)</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* inputLayer = network->AddInputLayer(0, <span class="stringliteral">"Input"</span>);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* weightsInputLayer = network->AddInputLayer(1, <span class="stringliteral">"Weights_Input"</span>);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* biasLayer = network->AddConstantLayer(biasConstantTensor, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, <span class="stringliteral">"Fully_Connected"</span>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0, <span class="stringliteral">"Output"</span>);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(weightsInputLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> }</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateFullyConnectedNetworkConstWeightsNonConstBias(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& weightsTensorInfo,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& biasTensorInfo,</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weightsConstantTensor,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor)</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* inputLayer = network->AddInputLayer(0, <span class="stringliteral">"Input"</span>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* weightsLayer = network->AddConstantLayer(weightsConstantTensor, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* biasLayer = network->AddInputLayer(2, <span class="stringliteral">"Bias_Input"</span>);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, <span class="stringliteral">"Fully_Connected"</span>);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0, <span class="stringliteral">"Output"</span>);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(weightsLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);</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="keywordflow">return</span> network;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> }</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="keywordtype">void</span> FullyConnectedWithDynamicWeightsEndToEnd(<span class="keyword">const</span> std::vector<armnn::BackendId>& backends)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 2, 3 }, ArmnnType);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.1f);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(63);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2 }, ArmnnType);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(5.f);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(10);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsTensorInfo({ 2, 6 }, ArmnnType);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.2f);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(93);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::vector<T> inputData {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  -1.2f, 6.1f, -3.5f,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  18.8f, -5.5f, 2.9f</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> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  std::vector<T> weightsData {</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  };</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  std::vector<T> floatExpectedOutputData {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  -107.04f, 110.f</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  };</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData);</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="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNonConstWeights(inputTensorInfo,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  outputTensorInfo,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  weightsTensorInfo,</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  descriptor);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  CHECK(network);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0, inputData }, {1, weightsData}};</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutputData }};</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  inputTensorData,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  expectedOutputTensorData,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  backends,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  1.0f);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> }</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> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="keywordtype">void</span> FullyConnectedWithDynamicOrConstantInputsEndToEnd(<span class="keyword">const</span> std::vector<armnn::BackendId>& backends,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> transposeWeights,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> constantWeightsOrBias)</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> {</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels };</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { inputChannels, outputChannels };</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">if</span> (transposeWeights)</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>  <a class="code" href="namespacearmnn.xhtml#a14d7f180bf51e86850305965c3707e07">std::swap</a>(weightsShape[0], weightsShape[1]);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  }</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { outputChannels };</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  std::vector<float> input =</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  };</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  std::vector<float> weights =</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  .5f, 2.f, .5f,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  .5f, 2.f, 1.f,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  .5f, 2.f, 2.f,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  .5f, 2.f, 3.f,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  .5f, 2.f, 4.f</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  };</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>  <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  weights =</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  {</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  .5f, .5f, .5f, .5f, .5f,</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  2.f, 2.f, 2.f, 2.f, 2.f,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  .5f, 1.f, 2.f, 3.f, 4.f</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>  }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  std::vector<float> biasValues = std::vector<float>({10.f, 20.f, 30.f});</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>  std::vector<float> expectedOutput =</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  {</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2]</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  };</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_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = transposeWeights;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> = constantWeightsOrBias;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">if</span> (!constantWeightsOrBias)</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">// Tests non constant weights and constant bias.</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biasConstantTensor(biasesDesc, biasValues.data());</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNonConstWeightsConstBias(inputTensorInfo,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  outputTensorInfo,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  weightsDesc,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  biasesDesc,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  biasConstantTensor,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  descriptor);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  CHECK(network);</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>  std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {1, weights}};</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }};</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  inputTensorData,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  expectedOutputTensorData,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  backends,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  1.0f);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  {</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// Tests constant weights and non constant bias.</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightsConstantTensor(weightsDesc, weights.data());</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkConstWeightsNonConstBias(inputTensorInfo,</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  outputTensorInfo,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  weightsDesc,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  biasesDesc,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  weightsConstantTensor,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  descriptor);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  CHECK(network);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {2, biasValues}};</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }};</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  inputTensorData,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  expectedOutputTensorData,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  backends,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  1.0f);</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> }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> } <span class="comment">// anonymous namespace</span></div><div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a14d7f180bf51e86850305965c3707e07"><div class="ttname"><a href="namespacearmnn.xhtml#a14d7f180bf51e86850305965c3707e07">armnn::swap</a></div><div class="ttdeci">void swap(OriginsDescriptor &first, OriginsDescriptor &second)</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00350">Descriptors.cpp:350</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="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00414">Descriptors.hpp:414</a></div></div> +<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div> +<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div> +<div class="ttc" id="_common_test_utils_8hpp_xhtml"><div class="ttname"><a href="_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00389">Descriptors.hpp:389</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::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#l00412">Descriptors.hpp:412</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="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00475">Tensor.cpp:475</a></div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</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_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00491">Tensor.cpp:491</a></div></div> +<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</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#l00172">INetwork.hpp:172</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00530">Network.cpp:530</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a2d3dcfc10f90adedc995b64211dab6e9"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">armnn::FullyConnectedDescriptor::m_ConstantWeights</a></div><div class="ttdeci">bool m_ConstantWeights</div><div class="ttdoc">Enable/disable constant weights and biases. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00416">Descriptors.hpp:416</a></div></div> +</div><!-- fragment --></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_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_797a213d7d01b98ef12d53b0820ea64e.xhtml">backendsCommon</a></li><li class="navelem"><a class="el" href="dir_28bfe507f7e135bdae07c2a6b7f66696.xhtml">test</a></li><li class="navelem"><a class="el" href="_fully_connected_end_to_end_test_impl_8hpp.xhtml">FullyConnectedEndToEndTestImpl.hpp</a></li> + <li class="footer">Generated on Tue Aug 24 2021 16:18:43 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> |