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<div class="title">FullyConnectedEndToEndTestImpl.hpp</div> </div>
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<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> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateFullyConnectedNetworkNoTensorInfoConstWeights(<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="l00088"></a><span class="lineno"> 88</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="l00089"></a><span class="lineno"> 89</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="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor)</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="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="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</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="l00095"></a><span class="lineno"> 95</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="l00096"></a><span class="lineno"> 96</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="l00097"></a><span class="lineno"> 97</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="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  weightsLayer-><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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateFullyConnectedNetworkNoConnectedWeightsExplicit(<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="l00107"></a><span class="lineno"> 107</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="l00108"></a><span class="lineno"> 108</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="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</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="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_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* inputLayer = network->AddInputLayer(0, <span class="stringliteral">"Input"</span>);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</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="l00115"></a><span class="lineno"> 115</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="l00116"></a><span class="lineno"> 116</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="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);</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>  <span class="keywordflow">return</span> network;</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> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateFullyConnectedNetworkNoConnectedWeightsAndBias(<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="l00126"></a><span class="lineno"> 126</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="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> 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>  <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="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</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="l00132"></a><span class="lineno"> 132</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="l00133"></a><span class="lineno"> 133</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="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">return</span> network;</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> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateFullyConnectedNetworkNoConnectedBiasExplicit(<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="l00142"></a><span class="lineno"> 142</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="l00143"></a><span class="lineno"> 143</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="l00144"></a><span class="lineno"> 144</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="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor)</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</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="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</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="l00150"></a><span class="lineno"> 150</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="l00151"></a><span class="lineno"> 151</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="l00152"></a><span class="lineno"> 152</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="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(weightsLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);</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">return</span> network;</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> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="keywordtype">void</span> FullyConnectedWithDynamicWeightsEndToEnd(<span class="keyword">const</span> std::vector<armnn::BackendId>& backends)</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> {</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 2, 3 }, ArmnnType);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.1f);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(63);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</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="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2 }, ArmnnType);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(5.f);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(10);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsTensorInfo({ 2, 6 }, ArmnnType);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.2f);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(93);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</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>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</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="l00182"></a><span class="lineno"> 182</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="l00183"></a><span class="lineno"> 183</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="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  std::vector<T> inputData {</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  -1.2f, 6.1f, -3.5f,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  18.8f, -5.5f, 2.9f</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> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  std::vector<T> weightsData {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f</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<T> floatExpectedOutputData {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  -107.04f, 110.f</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  };</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData);</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="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNonConstWeights(inputTensorInfo,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  outputTensorInfo,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  weightsTensorInfo,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  descriptor);</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>  CHECK(network);</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>  std::map<int, std::vector<T>> inputTensorData = {{ 0, inputData }, {1, weightsData}};</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutputData }};</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  inputTensorData,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  expectedOutputTensorData,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  backends,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  1.0f);</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> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="keywordtype">void</span> FullyConnectedWithDynamicOrConstantInputsEndToEnd(<span class="keyword">const</span> std::vector<armnn::BackendId>& backends,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> transposeWeights,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> constantWeightsOrBias)</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> {</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels };</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { inputChannels, outputChannels };</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="namespacearmnn.xhtml#a14d7f180bf51e86850305965c3707e07">std::swap</a>(weightsShape[0], weightsShape[1]);</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> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { outputChannels };</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</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>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</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="l00243"></a><span class="lineno"> 243</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>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</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>, 0.0f, 0, <span class="keyword">true</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>  std::vector<float> input =</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>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  5.0f, 4.0f, 3.0f, 2.0f, 1.0f</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> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  std::vector<float> weights =</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  .5f, 2.f, .5f,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  .5f, 2.f, 1.f,</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  .5f, 2.f, 2.f,</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  .5f, 2.f, 3.f,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  .5f, 2.f, 4.f</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  };</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>  <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  {</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  weights =</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  .5f, .5f, .5f, .5f, .5f,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  2.f, 2.f, 2.f, 2.f, 2.f,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  .5f, 1.f, 2.f, 3.f, 4.f</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</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> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  std::vector<float> biasValues = std::vector<float>({10.f, 20.f, 30.f});</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  std::vector<float> expectedOutput =</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  {</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2]</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  };</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</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="l00286"></a><span class="lineno"> 286</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = transposeWeights;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> = constantWeightsOrBias;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">if</span> (!constantWeightsOrBias)</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// Tests non constant weights and constant bias.</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biasConstantTensor(biasesDesc, biasValues.data());</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNonConstWeightsConstBias(inputTensorInfo,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  outputTensorInfo,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  weightsDesc,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  biasesDesc,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  biasConstantTensor,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  descriptor);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  CHECK(network);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {1, weights}};</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }};</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  inputTensorData,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  expectedOutputTensorData,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  backends,</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  1.0f);</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="keywordflow">else</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  {</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="comment">// Tests constant weights and non constant bias.</span></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightsConstantTensor(weightsDesc, weights.data());</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkConstWeightsNonConstBias(inputTensorInfo,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  outputTensorInfo,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  weightsDesc,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  biasesDesc,</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  weightsConstantTensor,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  descriptor);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  CHECK(network);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {2, biasValues}};</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }};</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  inputTensorData,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  expectedOutputTensorData,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  backends,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  1.0f);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  }</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> }</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> </div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <span class="keywordtype">void</span> FullyConnectedErrorChecking(<span class="keyword">const</span> std::vector<armnn::BackendId>& backends,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> explicitCheck,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> connectedWeights,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> connectedBias,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> tensorInfoSet)</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels };</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { inputChannels, outputChannels };</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { outputChannels };</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> </div><div class="line"><a name="l00357"></a><span class="lineno"> 357</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>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</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="l00359"></a><span class="lineno"> 359</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>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</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>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  std::vector<float> weights =</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>  .5f, 2.f, .5f,</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  .5f, 2.f, 1.f,</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  .5f, 2.f, 2.f,</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  .5f, 2.f, 3.f,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  .5f, 2.f, 4.f</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  };</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keywordflow">if</span>(explicitCheck)</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  {</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">if</span>(!biasEnabled)</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>  <span class="keywordflow">try</span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  CreateFullyConnectedNetworkNoConnectedWeightsExplicit(inputTensorInfo,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  outputTensorInfo,</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  biasesDesc,</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  descriptor);</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  FAIL(<span class="stringliteral">"LayerValidationException should have been thrown"</span>);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  }</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer_validation_exception.xhtml">LayerValidationException</a>& exc)</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>  CHECK(strcmp(exc.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>(), <span class="stringliteral">"Tried to connect bias to FullyConnected layer when bias is not enabled: "</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="stringliteral">"Failed to connect to input slot 2 on FullyConnected layer "</span></div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="stringliteral">"\"Fully_Connected\" as the slot does not exist or is unavailable"</span>) == 0);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  }</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (!connectedWeights)</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  {</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNoConnectedWeightsExplicit(inputTensorInfo,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  outputTensorInfo,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  biasesDesc,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  descriptor);</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  CHECK(network);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  {</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  FAIL(<span class="stringliteral">"LayerValidationException should have been thrown"</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>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer_validation_exception.xhtml">LayerValidationException</a>& exc)</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>  CHECK(strcmp(exc.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>(), <span class="stringliteral">"Fully_Connected layer weights not set: Input slot(s) 1 not connected "</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="stringliteral">"to an output slot on FullyConnected layer \"Fully_Connected\""</span>) == 0);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  }</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  }</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (!connectedBias)</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  {</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="comment">// Tests with constant weights.</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightsConstantTensor(weightsDesc, weights.data());</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNoConnectedBiasExplicit(inputTensorInfo,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  outputTensorInfo,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  weightsDesc,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  weightsConstantTensor,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  descriptor);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  CHECK(network);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordflow">try</span></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="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  FAIL(<span class="stringliteral">"LayerValidationException should have been thrown"</span>);</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>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer_validation_exception.xhtml">LayerValidationException</a>& exc)</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  {</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  CHECK(strcmp(exc.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>(), <span class="stringliteral">"Fully_Connected layer bias not set: Input slot(s) 2 not connected "</span></div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="stringliteral">"to an output slot on FullyConnected layer \"Fully_Connected\""</span>) == 0);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  }</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  }</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(!connectedWeights && !connectedBias)</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNoConnectedWeightsAndBias(inputTensorInfo,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  outputTensorInfo,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  descriptor);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  CHECK(network);</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span> </div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  FAIL(<span class="stringliteral">"LayerValidationException should have been thrown"</span>);</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>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer_validation_exception.xhtml">LayerValidationException</a>& exc)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  CHECK(strcmp(exc.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>(), <span class="stringliteral">"Fully_Connected layer weights and bias not set: Input slot(s) 1 & 2 not "</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="stringliteral">"connected to an output slot on FullyConnected layer \"Fully_Connected\""</span>) == 0);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  }</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(!tensorInfoSet)</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">// Tests with constant weights.</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightsConstantTensor(weightsDesc, weights.data());</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> </div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = CreateFullyConnectedNetworkNoTensorInfoConstWeights(inputTensorInfo,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  outputTensorInfo,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  weightsConstantTensor,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  descriptor);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  CHECK(network);</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">try</span></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="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  FAIL(<span class="stringliteral">"LayerValidationException should have been thrown"</span>);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  }</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer_validation_exception.xhtml">LayerValidationException</a>& exc)</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  {</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  CHECK(strcmp(exc.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>(), <span class="stringliteral">"Output slot TensorInfo not set on Constant layer \"Weights\""</span>) == 0);</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>  }</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> }</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> } <span class="comment">// anonymous namespace</span></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#l00040">Runtime.cpp:40</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#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#l00406">Descriptors.hpp:406</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#l00031">IRuntime.hpp:31</a></div></div>
<div class="ttc" id="classarmnn_1_1_exception_xhtml_abf843cbb29dec939d0731e491bab6f70"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">armnn::Exception::what</a></div><div class="ttdeci">virtual const char * what() const noexcept override</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8cpp_source.xhtml#l00032">Exceptions.cpp:32</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__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.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="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#l01605">Network.cpp:1605</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#l00381">Descriptors.hpp:381</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_validation_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_validation_exception.xhtml">armnn::LayerValidationException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00105">Exceptions.hpp:105</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#l00404">Descriptors.hpp:404</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="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#l00108">IRuntime.hpp:108</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#l00516">Tensor.cpp:516</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & 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_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#l00197">INetwork.hpp:197</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="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#l00478">Network.cpp:478</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#l00408">Descriptors.hpp:408</a></div></div>
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