Updating Doxygen documentation for 20.05 release.
Change-Id: I4d624343ed5fd6ae269c3d53532903084508fd14
Signed-off-by: Colm Donelan <Colm.Donelan@arm.com>
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+<div class="title">NormalizationTestImpl.cpp</div> </div>
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+<a href="_normalization_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. 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="_normalization_test_impl_8hpp.xhtml">NormalizationTestImpl.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> <span class="preprocessor">#include <<a class="code" href="_exceptions_8hpp.xhtml">armnn/Exceptions.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_layer_support_8hpp.xhtml">armnn/LayerSupport.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="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</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="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> SimpleNormalizationTestImpl(</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</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_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a> normChannel,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a> normMethod)</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#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 2;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };</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>  <span class="keyword">auto</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="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">auto</span> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> ret(outputTensorInfo);</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>  <span class="keyword">auto</span> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// Batch #0</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  1.0f, 2.0f,</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  3.0f, 4.0f,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// Batch #1</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  5.0f, 6.0f,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  7.0f, 8.0f</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  }));</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">float</span> alpha = 1.f;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">float</span> beta = 1.f;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordtype">float</span> kappa = 1.f;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  uint32_t normSize = 3;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);</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>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a> data;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</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_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = normChannel;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</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_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = normMethod;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</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_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = normSize;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</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_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = alpha;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</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_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = beta;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</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_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = kappa;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</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_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="classarmnn_1_1_passthrough_cpu_tensor_handle.xhtml">armnn::PassthroughCpuTensorHandle</a> refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  inputHandle->Allocate();</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  outputHandle->Allocate();</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="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</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="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">switch</span> (normMethod)</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>:</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">switch</span> (normChannel)</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a37bac6dce4f46277d89bfa3003e2e39b">armnn::NormalizationAlgorithmChannel::Within</a>:</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>  <span class="comment">// When normalising within channels, the 3x3 kernel covers the entire 2x2 input at every index.</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="comment">// Therefore, all output values should equal the inputs, but divided by:</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="comment">// pow((kappa + (accumulatedScale * alpha)), beta)</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// ...where accumulatedScale is the sum of every element squared.</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordtype">float</span> divisor[inputNum];</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < boost::numeric_cast<int>(inputNum); i++)</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>  <span class="keywordtype">float</span> accumulatedScale = input[i][0][0][0]*input[i][0][0][0] +</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  input[i][0][0][1]*input[i][0][0][1] +</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  input[i][0][1][0]*input[i][0][1][0] +</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  input[i][0][1][1]*input[i][0][1][1];</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  divisor[i] = powf((kappa + accumulatedScale * alpha), beta);</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>  ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo,</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::vector<float>({input[0][0][0][0]/divisor[0],</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  input[0][0][0][1]/divisor[0],</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  input[0][0][1][0]/divisor[0],</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  input[0][0][1][1]/divisor[0],</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  input[1][0][0][0]/divisor[1],</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  input[1][0][0][1]/divisor[1],</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  input[1][0][1][0]/divisor[1],</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  input[1][0][1][1]/divisor[1]}));</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a>:</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>  <span class="comment">// When normalising across channels, all output values should equal the inputs, but multiplied by:</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// pow((kappa + (accumulatedScale * alpha)), -beta)</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// ...where accumulatedScale is the sum of the inputs for adjacent channels for this element squared</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// ...where adjacent channels means within half the normSize for the channel</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// The test data has only one channel, so this is simplified below.</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  std::vector<float> outputVector;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0; n < boost::numeric_cast<int>(inputNum); ++n)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> h = 0; h < boost::numeric_cast<int>(inputHeight); ++h)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> w = 0; w < boost::numeric_cast<int>(inputWidth); ++w)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordtype">float</span> accumulatedScale = input[n][0][h][w]*input[n][0][h][w];</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordtype">float</span> scale = powf((kappa + accumulatedScale * alpha), -beta);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  outputVector.push_back(input[n][0][h][w] * scale);</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>  }</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>  ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputVector);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  }</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  {</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>(<span class="stringliteral">"Unsupported normalisation channel type, "</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="stringliteral">"only Across and Within are supported"</span>);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  }</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9aa94d2fcabc6b001015aeddfa19266e6f">armnn::NormalizationAlgorithmMethod::LocalContrast</a>: <span class="comment">// NOTE: intentional fallthrough.</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>(<span class="stringliteral">"Unsupported normalisation method type, "</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="stringliteral">"only LocalBrightness is supported"</span>);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  }</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> SimpleNormalizationNhwcTestImpl(</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</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="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a> normChannel,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a> normMethod)</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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 2;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputHeight, inputWidth, inputChannels };</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputHeight, outputWidth, outputChannels };</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">auto</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="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">auto</span> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> ret(outputTensorInfo);</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>  <span class="keyword">auto</span> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="comment">// Batch #0</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  1.0f, 2.0f,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  3.0f, 4.0f,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="comment">// Batch #1</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  5.0f, 6.0f,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  7.0f, 8.0f</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  }));</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordtype">float</span> alpha = 1.f;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordtype">float</span> beta = 1.f;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordtype">float</span> kappa = 1.f;</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  uint32_t normSize = 3;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a> data;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</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_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = normChannel;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</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_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = normMethod;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</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_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = normSize;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</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_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = alpha;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</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_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = beta;</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</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_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = kappa;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</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_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="classarmnn_1_1_passthrough_cpu_tensor_handle.xhtml">armnn::PassthroughCpuTensorHandle</a> refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info);</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>  inputHandle->Allocate();</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  outputHandle->Allocate();</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>  <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="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</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">switch</span> (normMethod)</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>:</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>  <span class="keywordflow">switch</span> (normChannel)</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  {</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a>:</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>  std::vector<float> expectedOutput{ 0.5f, 0.400000006f, 0.300000012f, 0.235294119f,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f };</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, expectedOutput);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  }</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keywordflow">default</span>:</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>(<span class="stringliteral">"Unsupported normalisation channel type, "</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="stringliteral">"Only Cross-map is supported for NHWC layout"</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>  }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  }</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9aa94d2fcabc6b001015aeddfa19266e6f">armnn::NormalizationAlgorithmMethod::LocalContrast</a>: <span class="comment">// NOTE: intentional fallthrough.</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  {</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>(<span class="stringliteral">"Unsupported normalisation method type, "</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="stringliteral">"only LocalBrightness is supported"</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>  }</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>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> CompareNormalizationTestImpl(</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</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="l00269"></a><span class="lineno"> 269</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a> normChannel,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a> normMethod)</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>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 5;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 32;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 24;</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>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</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>  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="l00290"></a><span class="lineno"> 290</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> ret(outputTensorInfo);</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>  <span class="keyword">auto</span> input = MakeRandomTensor<float, 4>(inputTensorInfo, 111234);</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>  constexpr <span class="keywordtype">float</span> alpha = 1.f;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  constexpr <span class="keywordtype">float</span> beta = 1.f;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  constexpr <span class="keywordtype">float</span> kappa = 1.f;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  constexpr uint32_t normSize = 5;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a> data;</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</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_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = normChannel;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</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_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = normMethod;</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</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_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = normSize;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</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_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = alpha;</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</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_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = beta;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</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_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = kappa;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</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>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</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>  <span class="comment">// Don't execute if Normalization is not supported for the method and channel types, as an exception will be raised.</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a> backend = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>();</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> reasonIfUnsupportedMaxLen = 255;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keywordtype">char</span> reasonIfUnsupported[reasonIfUnsupportedMaxLen+1];</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  ret.supported = <a class="code" href="namespacearmnn.xhtml#a754b0ac19fd6341ce2b5f480c3b35e8e">armnn::IsNormalizationSupported</a>(backend, inputTensorInfo, outputTensorInfo, data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  reasonIfUnsupported, reasonIfUnsupportedMaxLen);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keywordflow">if</span> (!ret.supported)</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  {</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="keywordflow">return</span> ret;</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>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a185c215631e1b01a6d41232410de4c46">CreateNormalization</a>(data, info);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a185c215631e1b01a6d41232410de4c46">CreateNormalization</a>(refData, refInfo);</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>  outputHandleRef->Allocate();</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  inputHandleRef->Allocate();</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  inputHandle->Allocate();</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  outputHandle->Allocate();</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>  <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="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  ExecuteWorkload(*workload, memoryManager);</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>  workloadRef->Execute();</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.outputExpected[0][0][0][0], outputHandleRef.get());</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> </div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"><a class="line" href="_normalization_test_impl_8hpp.xhtml#a3a13dd345b61e49ae26fcd7307cf1bd6"> 358</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> <a class="code" href="_normalization_test_impl_8cpp.xhtml#acbe7daf56bd75945713848779af20fcb">SimpleNormalizationAcrossTest</a>(</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</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="l00361"></a><span class="lineno"> 361</span> {</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">auto</span> normMethod = <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">auto</span> normChannel = <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a>;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordflow">return</span> SimpleNormalizationTestImpl(workloadFactory, memoryManager, normChannel, normMethod);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> }</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> </div><div class="line"><a name="l00367"></a><span class="lineno"><a class="line" href="_normalization_test_impl_8hpp.xhtml#a67aa048976c0286b2c9323019e9b4a57"> 367</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> <a class="code" href="_normalization_test_impl_8cpp.xhtml#a36dffbd3811b8524b9e288ce693198d4">SimpleNormalizationWithinTest</a>(</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</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="l00370"></a><span class="lineno"> 370</span> {</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">auto</span> normMethod = <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keyword">auto</span> normChannel = <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a37bac6dce4f46277d89bfa3003e2e39b">armnn::NormalizationAlgorithmChannel::Within</a>;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">return</span> SimpleNormalizationTestImpl(workloadFactory, memoryManager, normChannel, normMethod);</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> }</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"><a class="line" href="_normalization_test_impl_8hpp.xhtml#a68edd9ff85ae512a89892ce7ef8dafaa"> 376</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> <a class="code" href="_normalization_test_impl_8cpp.xhtml#a68edd9ff85ae512a89892ce7ef8dafaa">SimpleNormalizationAcrossNhwcTest</a>(</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</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="l00379"></a><span class="lineno"> 379</span> {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keyword">auto</span> normMethod = <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>;</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">auto</span> normChannel = <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a>;</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keywordflow">return</span> SimpleNormalizationNhwcTestImpl(workloadFactory, memoryManager, normChannel, normMethod);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> }</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"><a class="line" href="_normalization_test_impl_8hpp.xhtml#a1fa83d8d129f8af6f609f08c21646d4f"> 385</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> <a class="code" href="_normalization_test_impl_8cpp.xhtml#a1cb6617afdbe9139185122b93132acba">CompareNormalizationTest</a>(</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</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="l00388"></a><span class="lineno"> 388</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a> normChannel,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a> normMethod)</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>  <span class="keywordflow">return</span> CompareNormalizationTestImpl(workloadFactory, memoryManager, refWorkloadFactory, normChannel, normMethod);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</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="classarmnn_1_1_i_workload_factory_xhtml_a9f7e4296485d2812e7996089149c96d1"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">armnn::IWorkloadFactory::GetBackendId</a></div><div class="ttdeci">virtual const BackendId & GetBackendId() const =0</div></div>
+<div class="ttc" id="_normalization_test_impl_8hpp_xhtml"><div class="ttname"><a href="_normalization_test_impl_8hpp.xhtml">NormalizationTestImpl.hpp</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8526ea7cf860d8e7f8340e9f9354f9f0"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">armnn::NormalizationDescriptor::m_K</a></div><div class="ttdeci">float m_K</div><div class="ttdoc">Kappa value used for the across channel normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00593">Descriptors.hpp:593</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#l00053">Tensor.hpp:53</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#l00021">WorkloadFactory.hpp:21</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_normalization_descriptor_xhtml_a174279be57d7596eeb04c6b7f7510f99"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">armnn::NormalizationDescriptor::m_Alpha</a></div><div class="ttdeci">float m_Alpha</div><div class="ttdoc">Alpha value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00589">Descriptors.hpp:589</a></div></div>
+<div class="ttc" id="_normalization_test_impl_8cpp_xhtml_a68edd9ff85ae512a89892ce7ef8dafaa"><div class="ttname"><a href="_normalization_test_impl_8cpp.xhtml#a68edd9ff85ae512a89892ce7ef8dafaa">SimpleNormalizationAcrossNhwcTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_normalization_test_impl_8cpp_source.xhtml#l00376">NormalizationTestImpl.cpp:376</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a></div><div class="ttdeci">NormalizationAlgorithmChannel</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00126">Types.hpp:126</a></div></div>
+<div class="ttc" id="classarmnn_1_1_unimplemented_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00098">Exceptions.hpp:98</a></div></div>
+<div class="ttc" id="_normalization_test_impl_8cpp_xhtml_acbe7daf56bd75945713848779af20fcb"><div class="ttname"><a href="_normalization_test_impl_8cpp.xhtml#acbe7daf56bd75945713848779af20fcb">SimpleNormalizationAcrossTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleNormalizationAcrossTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_normalization_test_impl_8cpp_source.xhtml#l00358">NormalizationTestImpl.cpp:358</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00595">Descriptors.hpp:595</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_a754b0ac19fd6341ce2b5f480c3b35e8e"><div class="ttname"><a href="namespacearmnn.xhtml#a754b0ac19fd6341ce2b5f480c3b35e8e">armnn::IsNormalizationSupported</a></div><div class="ttdeci">bool IsNormalizationSupported(const BackendId &backend, const TensorInfo &input, const TensorInfo &output, const NormalizationDescriptor &descriptor, char *reasonIfUnsupported=nullptr, size_t reasonIfUnsupportedMaxLength=1024)</div><div class="ttdoc">Deprecated in favor of IBackend and ILayerSupport interfaces. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_8cpp_source.xhtml#l00448">LayerSupport.cpp:448</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</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#l00049">WorkloadData.hpp:49</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a05945f080edf694b631960728b87aadb"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">armnn::NormalizationDescriptor::m_NormMethodType</a></div><div class="ttdeci">NormalizationAlgorithmMethod m_NormMethodType</div><div class="ttdoc">Normalization method algorithm to use (LocalBrightness, LocalContrast). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00585">Descriptors.hpp:585</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="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#l00090">IBackendInternal.hpp:90</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a185c215631e1b01a6d41232410de4c46"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a185c215631e1b01a6d41232410de4c46">armnn::IWorkloadFactory::CreateNormalization</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateNormalization(const NormalizationQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01436">WorkloadFactory.cpp:1436</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="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo, const bool IsMemoryManaged=true) const =0</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="structarmnn_1_1_normalization_descriptor_xhtml_afe1f0f09d49ad2befc01f8789187b7dd"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">armnn::NormalizationDescriptor::m_NormChannelType</a></div><div class="ttdeci">NormalizationAlgorithmChannel m_NormChannelType</div><div class="ttdoc">Normalization channel algorithm to use (Across, Within). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00583">Descriptors.hpp:583</a></div></div>
+<div class="ttc" id="_layer_support_8hpp_xhtml"><div class="ttname"><a href="_layer_support_8hpp.xhtml">LayerSupport.hpp</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
+<div class="ttc" id="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9aa94d2fcabc6b001015aeddfa19266e6f"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9aa94d2fcabc6b001015aeddfa19266e6f">armnn::NormalizationAlgorithmMethod::LocalContrast</a></div><div class="ttdoc">Jarret 2009: Local Contrast Normalization. </div></div>
+<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437a37bac6dce4f46277d89bfa3003e2e39b"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a37bac6dce4f46277d89bfa3003e2e39b">armnn::NormalizationAlgorithmChannel::Within</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="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="_normalization_test_impl_8cpp_xhtml_a36dffbd3811b8524b9e288ce693198d4"><div class="ttname"><a href="_normalization_test_impl_8cpp.xhtml#a36dffbd3811b8524b9e288ce693198d4">SimpleNormalizationWithinTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleNormalizationWithinTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_normalization_test_impl_8cpp_source.xhtml#l00367">NormalizationTestImpl.cpp:367</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a></div><div class="ttdoc">Krichevsky 2012: Local Brightness Normalization. </div></div>
+<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a></div><div class="ttdeci">NormalizationAlgorithmMethod</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00132">Types.hpp:132</a></div></div>
+<div class="ttc" id="classarmnn_1_1_passthrough_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_passthrough_cpu_tensor_handle.xhtml">armnn::PassthroughCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00138">CpuTensorHandle.hpp:138</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::NormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_aa70c05f1aad12fbd9d9ec43ea4557b03"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">armnn::NormalizationDescriptor::m_NormSize</a></div><div class="ttdeci">uint32_t m_NormSize</div><div class="ttdoc">Depth radius value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00587">Descriptors.hpp:587</a></div></div>
+<div class="ttc" id="_normalization_test_impl_8cpp_xhtml_a1cb6617afdbe9139185122b93132acba"><div class="ttname"><a href="_normalization_test_impl_8cpp.xhtml#a1cb6617afdbe9139185122b93132acba">CompareNormalizationTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > CompareNormalizationTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, armnn::NormalizationAlgorithmChannel normChannel, armnn::NormalizationAlgorithmMethod normMethod)</div><div class="ttdef"><b>Definition:</b> <a href="_normalization_test_impl_8cpp_source.xhtml#l00385">NormalizationTestImpl.cpp:385</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
+<div 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>
+<div class="ttc" id="classarmnn_1_1_backend_id_xhtml"><div class="ttname"><a href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00075">BackendId.hpp:75</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00210">WorkloadData.hpp:210</a></div></div>
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