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authorNikhil Raj <nikhil.raj@arm.com>2022-05-24 11:32:07 +0100
committerNikhil Raj <nikhil.raj@arm.com>2022-05-24 11:32:07 +0100
commit549b9600a6eaf0727fa084465a75f173edf8f381 (patch)
tree9c9b054417504444fff067b74eaa1811b74e6d06 /22.05/_redirect_members_to_constant_inputs_tests_8cpp_source.xhtml
parentf4019872c1134c6fcc1d6993e5746f55c1e79208 (diff)
downloadarmnn-549b9600a6eaf0727fa084465a75f173edf8f381.tar.gz
Update 22.05 Doxygen Docs after updates to main Readme
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com> Change-Id: I56711772406a41ff81fa136a5fb6c59c9b9cf504
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+<a href="_redirect_members_to_constant_inputs_tests_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<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>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &lt;TestUtils.hpp&gt;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_optimizer_8hpp.xhtml">Optimizer.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &lt;doctest/doctest.h&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;</div><div class="line"><a name="l00012"></a><span class="lineno"><a class="line" href="_redirect_members_to_constant_inputs_tests_8cpp.xhtml#a77a062dba8ec73047ae4e734519f5ef8"> 12</a></span>&#160;<a class="code" href="_redirect_members_to_constant_inputs_tests_8cpp.xhtml#a77a062dba8ec73047ae4e734519f5ef8">TEST_SUITE</a>(<span class="stringliteral">&quot;Optimizer&quot;</span>)</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;{</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a>;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;TEST_CASE(<span class="stringliteral">&quot;RedirectMembersToConstantInputsFullyConnectedTest&quot;</span>)</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;{</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160; <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> graph;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo ({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; <span class="comment">// Check if isConstant is enabled for weights and biases tensor info.</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160; CHECK(weightsInfo.IsConstant());</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; CHECK(biasesInfo.IsConstant());</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; desc.<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="l00031"></a><span class="lineno"> 31</span>&#160; desc.m_ConstantWeights = <span class="keyword">false</span>;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; <span class="comment">// Create the simple test network with Weights and Biases as inputs to a FullyConnected layer.</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="keyword">auto</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a>&gt;(0, <span class="stringliteral">&quot;Input&quot;</span>);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; <span class="keyword">auto</span> weights = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_constant_layer.xhtml">armnn::ConstantLayer</a>&gt;(<span class="stringliteral">&quot;Weights&quot;</span>);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="keyword">auto</span> biases = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_constant_layer.xhtml">armnn::ConstantLayer</a>&gt;(<span class="stringliteral">&quot;Biases&quot;</span>);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <span class="keyword">auto</span> fcLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a>&gt;(desc, <span class="stringliteral">&quot;FullyConnected&quot;</span>);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; <span class="keyword">auto</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a>&lt;<a class="code" href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a>&gt;(1, <span class="stringliteral">&quot;Output&quot;</span>);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="keywordtype">float</span> expectedWeightsData[] = { 1.0f, 1.0f, 1.0f, 1.0f };</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="keywordtype">float</span> expectedBiasesData[] = { 2.0f, 2.0f };</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="comment">// Set the m_LayerOutput for the optimizer to point to.</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weightsTensor(weightsInfo, &amp;expectedWeightsData);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biasesTensor(biasesInfo, &amp;expectedBiasesData);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; weights-&gt;m_LayerOutput = std::make_unique&lt;armnn::ScopedTensorHandle&gt;(weightsTensor);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; biases-&gt;<a class="code" href="classarmnn_1_1_constant_layer.xhtml#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a> = std::make_unique&lt;armnn::ScopedTensorHandle&gt;(biasesTensor);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; input-&gt;GetOutputSlot().SetTensorInfo(inputInfo);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; weights-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(weightsInfo);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; biases-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(biasesInfo);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; fcLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <span class="comment">// Connect up the layers</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; input-&gt;GetOutputSlot(0).Connect(fcLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; weights-&gt;<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>(fcLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1));</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; biases-&gt;<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>(fcLayer-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(2));</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; fcLayer-&gt;<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>(output-&gt;<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="comment">// Member variables should be null before optimization.</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; CHECK(fcLayer-&gt;m_Weight == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; CHECK(fcLayer-&gt;m_Bias == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="comment">// Run the optimizer</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a27f926297aa7a695e877932211bc3f36">RedirectMembersToConstantInputs</a>()));</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="comment">// Check if member variables are not null and shape is set correctly.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; CHECK(fcLayer-&gt;m_Weight != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; CHECK(fcLayer-&gt;m_Bias != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; CHECK(fcLayer-&gt;m_Weight-&gt;GetTensorInfo().GetShape() == weightsInfo.GetShape());</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; CHECK(fcLayer-&gt;m_Bias-&gt;GetTensorInfo().GetShape() == biasesInfo.GetShape());</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="comment">// Check whether data matches expected float data</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span>* weightsData = fcLayer-&gt;m_Weight-&gt;GetConstTensor&lt;<span class="keywordtype">float</span>&gt;();</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; CHECK(weightsData[0] == expectedWeightsData[0]);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; CHECK(weightsData[1] == expectedWeightsData[1]);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; CHECK(weightsData[2] == expectedWeightsData[2]);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; CHECK(weightsData[3] == expectedWeightsData[3]);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span>* biasesData = fcLayer-&gt;m_Bias-&gt;GetConstTensor&lt;<span class="keywordtype">float</span>&gt;();</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; CHECK(biasesData[0] == expectedBiasesData[0]);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; CHECK(biasesData[1] == expectedBiasesData[1]);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;}</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;}</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="namespacearmnn_xhtml_aa7427025a851113a492de0b68b23d22a"><div class="ttname"><a href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a></div><div class="ttdeci">Optimizer::Optimizations MakeOptimizations(Args &amp;&amp;... args)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00043">Optimizer.hpp:43</a></div></div>
+<div class="ttc" id="classarmnn_1_1_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="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&lt; ConstTensorHandle &gt; m_LayerOutput</div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.xhtml#l00048">ConstantLayer.hpp:48</a></div></div>
+<div class="ttc" id="classarmnn_1_1_graph_xhtml_a7563c5b899e7d0ada08fd0fdb202f205"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">armnn::Graph::AddLayer</a></div><div class="ttdeci">LayerT * AddLayer(Args &amp;&amp;... args)</div><div class="ttdoc">Adds a new layer, of type LayerType, to the graph constructed with the arguments passed. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00425">Graph.hpp:425</a></div></div>
+<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_adcfb97035799ea4c043f9ef370714815"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">armnn::OutputSlot::Connect</a></div><div class="ttdeci">int Connect(InputSlot &amp;destination)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#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 &amp;graph, const Optimizations &amp;optimizations)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8cpp_source.xhtml#l00016">Optimizer.cpp:16</a></div></div>
+<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a27f926297aa7a695e877932211bc3f36"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a27f926297aa7a695e877932211bc3f36">armnn::optimizations::RedirectMembersToConstantInputs</a></div><div class="ttdeci">OptimizeForType&lt; Layer, RedirectMembersToConstantInputsImpl &gt; RedirectMembersToConstantInputs</div><div class="ttdef"><b>Definition:</b> <a href="_redirect_members_to_constant_inputs_8hpp_source.xhtml#l00085">RedirectMembersToConstantInputs.hpp:85</a></div></div>
+<div class="ttc" id="_optimizer_8hpp_xhtml"><div class="ttname"><a href="_optimizer_8hpp.xhtml">Optimizer.hpp</a></div></div>
+<div class="ttc" id="classarmnn_1_1_layer_xhtml_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">armnn::Layer::GetInputSlot</a></div><div class="ttdeci">const InputSlot &amp; GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00322">Layer.hpp:322</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="classarmnn_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a></div><div class="ttdoc">This layer represents a fully connected operation. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_8hpp_source.xhtml#l00015">FullyConnectedLayer.hpp:15</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#l00468">Descriptors.hpp:468</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#l00491">Descriptors.hpp:491</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="_redirect_members_to_constant_inputs_tests_8cpp_xhtml_a77a062dba8ec73047ae4e734519f5ef8"><div class="ttname"><a href="_redirect_members_to_constant_inputs_tests_8cpp.xhtml#a77a062dba8ec73047ae4e734519f5ef8">TEST_SUITE</a></div><div class="ttdeci">TEST_SUITE(&quot;Optimizer&quot;)</div><div class="ttdef"><b>Definition:</b> <a href="_redirect_members_to_constant_inputs_tests_8cpp_source.xhtml#l00012">RedirectMembersToConstantInputsTests.cpp:12</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_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 &amp;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_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot &amp; GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00324">Layer.hpp:324</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
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+ <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="_redirect_members_to_constant_inputs_tests_8cpp.xhtml">RedirectMembersToConstantInputsTests.cpp</a></li>
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