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<div class="title">FullyConnectedTestImpl.cpp</div> </div>
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<a href="_fully_connected_test_impl_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> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 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> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_fully_connected_test_impl_8hpp.xhtml">FullyConnectedTestImpl.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</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="_quantize_helper_8hpp.xhtml">QuantizeHelper.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="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.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="_data_type_utils_8hpp.xhtml">backendsCommon/test/DataTypeUtils.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_workload_test_utils_8hpp.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>></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 <<a class="code" href="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>></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="comment">//</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment">// Implementation templates</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment">//</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">template</span><<span class="keyword">typename</span> T, <span class="keyword">typename</span> B></div><div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="_fully_connected_test_impl_8cpp.xhtml#a4e96a07c721329ec7de9e11c2f3097be"> 24</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_fully_connected_test_impl_8cpp.xhtml#a4e96a07c721329ec7de9e11c2f3097be">SimpleFullyConnectedTestImpl</a>(</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc,</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  boost::multi_array<T, 2>& weights,</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  boost::multi_array<B, 1>& bias,</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  boost::multi_array<T, 4>& input,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordtype">bool</span> transposeWeights)</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>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</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="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">armnn::FullyConnectedQueueDescriptor</a> data;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(weightsDesc);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasesDesc);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &weights[0][0]);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  data.<a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  data.<a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = transposeWeights;</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>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a1c193739520e08f686b347ff795ad2fe">CreateFullyConnected</a>(data, info);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result(outputTensorInfo);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  inputHandle->Allocate();</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  outputHandle->Allocate();</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&result.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0], outputHandle.get());</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> }</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T></div><div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="_fully_connected_test_impl_8hpp.xhtml#a834305b5bfdbee9e753bb7ad299944cf"> 71</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_fully_connected_test_impl_8cpp.xhtml#a834305b5bfdbee9e753bb7ad299944cf">FullyConnectedTest</a>(</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordtype">bool</span> biasEnabled)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3u;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2u;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputWidth * inputHeight * inputChannels;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 2u;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.1f);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  inputTensorInfo.SetQuantizationOffset(63);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, outputChannels }, ArmnnType);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(5.f);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc({ outputChannels, inputSize }, ArmnnType);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  weightsDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.2f);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  weightsDesc.SetQuantizationOffset(93);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc({ outputChannels }, <a class="code" href="namespacearmnn.xhtml#a83c4a275acf59f62b8387f389d0929d5">GetBiasTypeFromWeightsType</a>(weightsDesc.GetDataType()).value());</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  biasesDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale());</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  biasesDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result(outputTensorInfo);</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="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>(</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>  -1.2f, 6.1f, -3.5f,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  18.8f, -5.5f, 2.9f</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  },</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  inputTensorInfo));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">auto</span> weights = MakeTensor<T, 2>(weightsDesc, ConvertToDataType<ArmnnType>(</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f,</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  },</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  weightsDesc));</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">auto</span> bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500});</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  result = SimpleFullyConnectedTestImpl<T>(</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  workloadFactory,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  memoryManager,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  tensorHandleFactory,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  weightsDesc, biasesDesc,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  weights, bias, input,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  biasEnabled, <a class="code" href="_cl_layer_tests_8cpp.xhtml#a88d84cc49602ca19491dea9c998035fe">true</a></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  );</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>  <span class="keywordflow">if</span> (biasEnabled)</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>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 2>(outputTensorInfo,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  ConvertToDataType<ArmnnType>({80.f, 1460.f}, outputTensorInfo));</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  result.outputExpected = MakeTensor<T, 2>(outputTensorInfo,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  ConvertToDataType<ArmnnType>({-107.04f, 110.f}, outputTensorInfo));</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</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>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> }</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="comment">//</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment">// ArmNN variant of the AndroidNN fully_connected_float_large test.</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment">//</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment">// Tests the fully connected layer with large values, optionally transposing weights.</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment">// Note this is templated for consistency, but the nature of this tests makes it unlikely to be useful in Uint8 mode.</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment">//</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00150"></a><span class="lineno"><a class="line" href="_fully_connected_test_impl_8cpp.xhtml#a72387040065b7cdcd1ab4b8068fd6b50"> 150</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_fully_connected_test_impl_8cpp.xhtml#a72387040065b7cdcd1ab4b8068fd6b50">FullyConnectedLargeTestCommon</a>(</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordtype">bool</span> transposeWeights,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  int32_t qOffset = 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="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 1;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 1;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 1;</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>  <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels };</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { inputChannels, outputChannels };</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  {</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="namespacearmnn.xhtml#a14d7f180bf51e86850305965c3707e07">std::swap</a>(weightsShape[0], weightsShape[1]);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { outputChannels };</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, ArmnnType);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, outputShape, ArmnnType);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  weightsDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, weightsShape, ArmnnType);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  biasesDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasShape, ArmnnType);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</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>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</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> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result(outputTensorInfo);</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>  boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  armnnUtils::QuantizedVector<T>({</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  },</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  qScale, qOffset)</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  );</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>  boost::multi_array<T, 2> weights = MakeTensor<T, 2>(weightsDesc,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  armnnUtils::QuantizedVector<T>({</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  2.0f, 3.0f, 4.0f, 5.0f, 6.0f</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  },</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  qScale, qOffset)</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  );</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  std::vector<T> biasValues({900000.f});</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasesDesc, biasValues);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  result = SimpleFullyConnectedTestImpl<T>(</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  workloadFactory,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  memoryManager,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  tensorHandleFactory,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  weightsDesc, biasesDesc,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  weights, bias, input,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">true</span>, transposeWeights</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  );</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 2>(outputTensorInfo,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  armnnUtils::QuantizedVector<T>({ 965432.0f }, qScale, qOffset));</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="comment">//</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="comment">// Explicit template specializations</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="comment">//</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> </div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8></a>, 2></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> FullyConnectedTest<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordtype">bool</span> biasEnabled);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16></a>, 2></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> FullyConnectedTest<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keywordtype">bool</span> biasEnabled);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="comment">//</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="comment">// Implementation functions</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="comment">//</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"><a class="line" href="_fully_connected_test_impl_8hpp.xhtml#afb505feb224a201164ab815b8a6159cf"> 253</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_fully_connected_test_impl_8cpp.xhtml#afb505feb224a201164ab815b8a6159cf">FullyConnectedFloat32Test</a>(</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordtype">bool</span> transposeWeights)</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels };</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { inputChannels, outputChannels };</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  {</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="namespacearmnn.xhtml#a14d7f180bf51e86850305965c3707e07">std::swap</a>(weightsShape[0], weightsShape[1]);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  }</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { outputChannels };</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  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="l00287"></a><span class="lineno"> 287</span>  weightsDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  biasesDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> result(outputTensorInfo);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  boost::multi_array<float, 4> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(</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>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> </div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  })</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  );</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  boost::multi_array<float, 2> weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(</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>  .5f, 2.f, .5f,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  .5f, 2.f, 1.f,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  .5f, 2.f, 2.f,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  .5f, 2.f, 3.f,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  .5f, 2.f, 4.f</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  }));</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">if</span> (transposeWeights)</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>  weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(</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>  .5f, .5f, .5f, .5f, .5f,</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  2.f, 2.f, 2.f, 2.f, 2.f,</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  .5f, 1.f, 2.f, 3.f, 4.f</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  }));</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  std::vector<float> biasValues({0.f, 0.f, 0.f});</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  {</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  biasValues = std::vector<float>({10.f, 20.f, 30.f});</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  }</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  boost::multi_array<float, 1> bias = MakeTensor<float, 1>(biasesDesc, biasValues);</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>  result = SimpleFullyConnectedTestImpl<float>(</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  workloadFactory,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  memoryManager,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  tensorHandleFactory,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  weightsDesc, biasesDesc,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  weights, bias, input,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  biasEnabled, transposeWeights</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  );</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<float, 2>(outputTensorInfo, std::vector<float>(</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  {</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],</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>  2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2]</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  })</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> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l00352"></a><span class="lineno"><a class="line" href="_fully_connected_test_impl_8hpp.xhtml#aa50f043364dd453338261e82397c3e1b"> 352</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_fully_connected_test_impl_8cpp.xhtml#aa50f043364dd453338261e82397c3e1b">FullyConnectedLargeTest</a>(</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordtype">bool</span> transposeWeights)</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> {</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">return</span> FullyConnectedLargeTestCommon<armnn::DataType::Float32>(workloadFactory,</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  memoryManager,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  tensorHandleFactory,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  transposeWeights);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> }</div><div class="ttc" id="_tensor_copy_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_copy_utils_8hpp.xhtml">TensorCopyUtils.hpp</a></div></div>
<div class="ttc" id="_fully_connected_test_impl_8cpp_xhtml_a834305b5bfdbee9e753bb7ad299944cf"><div class="ttname"><a href="_fully_connected_test_impl_8cpp.xhtml#a834305b5bfdbee9e753bb7ad299944cf">FullyConnectedTest</a></div><div class="ttdeci">LayerTestResult< T, 2 > FullyConnectedTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_test_impl_8cpp_source.xhtml#l00071">FullyConnectedTestImpl.cpp:71</a></div></div>
<div class="ttc" id="_fully_connected_test_impl_8cpp_xhtml_afb505feb224a201164ab815b8a6159cf"><div class="ttname"><a href="_fully_connected_test_impl_8cpp.xhtml#afb505feb224a201164ab815b8a6159cf">FullyConnectedFloat32Test</a></div><div class="ttdeci">LayerTestResult< float, 2 > FullyConnectedFloat32Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool biasEnabled, bool transposeWeights)</div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_test_impl_8cpp_source.xhtml#l00253">FullyConnectedTestImpl.cpp:253</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="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::FullyConnectedQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00173">WorkloadData.hpp:173</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="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.xhtml#l00022">WorkloadFactory.hpp:22</a></div></div>
<div class="ttc" id="_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</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#l00404">Descriptors.hpp:404</a></div></div>
<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array< T, n > outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00042">LayerTestResult.hpp:42</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">armnn::FullyConnectedQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00165">WorkloadData.hpp:165</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00057">WorkloadData.hpp:57</a></div></div>
<div class="ttc" id="_tensor_helpers_8hpp_xhtml"><div class="ttname"><a href="_tensor_helpers_8hpp.xhtml">TensorHelpers.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a83c4a275acf59f62b8387f389d0929d5"><div class="ttname"><a href="namespacearmnn.xhtml#a83c4a275acf59f62b8387f389d0929d5">armnn::GetBiasTypeFromWeightsType</a></div><div class="ttdeci">armnn::Optional< armnn::DataType > GetBiasTypeFromWeightsType(armnn::Optional< armnn::DataType > weightsType)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_rules_8hpp_source.xhtml#l00014">LayerSupportRules.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00092">IBackendInternal.hpp:92</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#l00402">Descriptors.hpp:402</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#l00464">Tensor.cpp:464</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
<div class="ttc" id="_fully_connected_test_impl_8hpp_xhtml"><div class="ttname"><a href="_fully_connected_test_impl_8hpp.xhtml">FullyConnectedTestImpl.hpp</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div>
<div class="ttc" id="struct_layer_test_result_xhtml_ac9d44d346bb7c89f7a7aa31d2bee947f"><div class="ttname"><a href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">LayerTestResult::output</a></div><div class="ttdeci">boost::multi_array< T, n > output</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_factory_8hpp_source.xhtml#l00041">ITensorHandleFactory.hpp:41</a></div></div>
<div class="ttc" id="_fully_connected_test_impl_8cpp_xhtml_aa50f043364dd453338261e82397c3e1b"><div class="ttname"><a href="_fully_connected_test_impl_8cpp.xhtml#aa50f043364dd453338261e82397c3e1b">FullyConnectedLargeTest</a></div><div class="ttdeci">LayerTestResult< float, 2 > FullyConnectedLargeTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool transposeWeights)</div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_test_impl_8cpp_source.xhtml#l00352">FullyConnectedTestImpl.cpp:352</a></div></div>
<div class="ttc" id="_fully_connected_test_impl_8cpp_xhtml_a72387040065b7cdcd1ab4b8068fd6b50"><div class="ttname"><a href="_fully_connected_test_impl_8cpp.xhtml#a72387040065b7cdcd1ab4b8068fd6b50">FullyConnectedLargeTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 2 > FullyConnectedLargeTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool transposeWeights, float qScale=0.0f, int32_t qOffset=0)</div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_test_impl_8cpp_source.xhtml#l00150">FullyConnectedTestImpl.cpp:150</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="_data_type_utils_8hpp_xhtml"><div class="ttname"><a href="_data_type_utils_8hpp.xhtml">DataTypeUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00030">LayerTestResult.hpp:30</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00480">Tensor.cpp:480</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a1c193739520e08f686b347ff795ad2fe"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a1c193739520e08f686b347ff795ad2fe">armnn::IWorkloadFactory::CreateFullyConnected</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateFullyConnected(const FullyConnectedQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01440">WorkloadFactory.cpp:1440</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::FullyConnectedQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00174">WorkloadData.hpp:174</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_a375f11dd42ff042435e8771cf287b20c"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">armnn::ITensorHandleFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0</div></div>
<div class="ttc" id="_cl_layer_tests_8cpp_xhtml_a88d84cc49602ca19491dea9c998035fe"><div class="ttname"><a href="_cl_layer_tests_8cpp.xhtml#a88d84cc49602ca19491dea9c998035fe">true</a></div><div class="ttdeci">DataLayout::NCHW DataLayout::NCHW DataLayout::NHWC DataLayout::NHWC true</div><div class="ttdef"><b>Definition:</b> <a href="_cl_layer_tests_8cpp_source.xhtml#l00212">ClLayerTests.cpp:212</a></div></div>
<div class="ttc" id="_fully_connected_test_impl_8cpp_xhtml_a4e96a07c721329ec7de9e11c2f3097be"><div class="ttname"><a href="_fully_connected_test_impl_8cpp.xhtml#a4e96a07c721329ec7de9e11c2f3097be">SimpleFullyConnectedTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 2 > SimpleFullyConnectedTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, armnn::TensorInfo inputTensorInfo, armnn::TensorInfo outputTensorInfo, armnn::TensorInfo weightsDesc, armnn::TensorInfo biasesDesc, boost::multi_array< T, 2 > &weights, boost::multi_array< B, 1 > &bias, boost::multi_array< T, 4 > &input, bool biasEnabled, bool transposeWeights)</div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_test_impl_8cpp_source.xhtml#l00024">FullyConnectedTestImpl.cpp:24</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
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