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<div class="title">L2NormalizationTestImpl.cpp</div>  </div>
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<a href="_l2_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>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_l2_normalization_test_impl_8hpp.xhtml">L2NormalizationTestImpl.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;</div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_utils_8hpp.xhtml">armnnUtils/TensorUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;</div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_test_utils_8hpp.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;{</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> L2NormalizationTestImpl(</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>&amp; inputOutputTensorShape,</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;    <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;    int32_t offset,</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt;&amp; inputValues,</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;    <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;    int32_t outOffset,</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt;&amp; expectedOutputValues,</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    <span class="keywordtype">float</span> epsilon = 1e-12f)</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(inputOutputTensorShape, ArmnnType, scale, offset);</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(inputOutputTensorShape, ArmnnType, outScale, outOffset);</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;    <span class="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    std::vector&lt;float&gt; inputData = inputValues;</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    {</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;        std::vector&lt;float&gt; tmp(inputData.size());</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;        <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(float));</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;        inputData = tmp;</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    }</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    <span class="keyword">auto</span> inputTensor = MakeTensor&lt;T, 4&gt;(inputTensorInfo,</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;                                        armnnUtils::QuantizedVector&lt;T&gt;(inputData,</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;                                                                       inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;                                                                       inputTensorInfo.GetQuantizationOffset()));</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    std::vector&lt;float&gt; expectedOutputData = expectedOutputValues;</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    {</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;        std::vector&lt;float&gt; tmp(expectedOutputData.size());</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;        <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, expectedOutputData.data(), tmp.data(),</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;                            <span class="keyword">sizeof</span>(float));</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;        expectedOutputData = tmp;</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    }</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    result.outputExpected =</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;        MakeTensor&lt;T, 4&gt;(outputTensorInfo,</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;                         armnnUtils::QuantizedVector&lt;T&gt;(expectedOutputData,</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;                                                        outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;                                                        outputTensorInfo.GetQuantizationOffset()));</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <a class="code" href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml">armnn::L2NormalizationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = epsilon;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    <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="l00078"></a><span class="lineno">   78</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a3c86f886e36ce943f1ebc241a37f0413">CreateL2Normalization</a>(descriptor, info);</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    inputHandle-&gt;Allocate();</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    outputHandle-&gt;Allocate();</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;inputTensor[0][0][0][0]);</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;result.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;}</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;<span class="keywordtype">float</span> CalcInvL2Norm(std::initializer_list&lt;float&gt; elements)</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;{</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> reduction = std::accumulate(elements.begin(), elements.end(), 0.0f,</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        [](<span class="keywordtype">float</span> acc, <span class="keywordtype">float</span> element) { <span class="keywordflow">return</span> acc + element * element; });</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    <span class="keywordflow">return</span> 1.0f / sqrtf(reduction);</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;}</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> L2NormalizationEpsilonTestCommon(</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;        int32_t offset,</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;        <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;        int32_t outOffset,</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;        <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;        <span class="keywordtype">float</span> epsilon)</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;{</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="comment">// Width: 1</span></div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="comment">// Height: 1</span></div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="comment">// Channels: 3</span></div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfBatches = 1;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfChannels = 3;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 1;</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 1;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a>(</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;            numberOfBatches, numberOfChannels, height, width, layout);</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="comment">// 0.0000001^2 + 0.00000002^2 + 0.00000003^2 &lt; 1e-12</span></div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    {</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (1) x Width (1)</span></div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;        0.00000001f,</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (1) x Width (1)</span></div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        0.00000002f,</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        <span class="comment">// Batch 0, Channel 2, Height (1) x Width (1)</span></div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        0.00000003f,</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    };</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> approxInvL2Norm = 1.f / sqrtf(epsilon);</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    {</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (1) x Width (1)</span></div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        0.00000001f * approxInvL2Norm,</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;        0.00000002f * approxInvL2Norm,</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        0.00000003f * approxInvL2Norm,</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    };</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    <span class="keywordflow">return</span> L2NormalizationTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;        workloadFactory,</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;        memoryManager,</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;        inputOutputShape,</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;        scale,</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;        offset,</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;        inputValues,</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        outScale,</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;        outOffset,</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;        expectedOutputValues,</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        layout,</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;        epsilon);</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;}</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> L2Normalization1dTestCommon(</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;        int32_t offset,</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;        int32_t outOffset,</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;{</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <span class="comment">// Width: 1</span></div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <span class="comment">// Height: 1</span></div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    <span class="comment">// Channels: 10</span></div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfBatches = 1;</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfChannels = 10;</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 1;</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 1;</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a>(</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;            numberOfBatches, numberOfChannels, height, width, layout);</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    {</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (1) x Width (1)</span></div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        1.0f,</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (1) x Width (1)</span></div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        2.0f,</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        <span class="comment">// Batch 0, Channel 2, Height (1) x Width (1)</span></div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        3.0f,</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        <span class="comment">// Batch 0, Channel 3, Height (1) x Width (1)</span></div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;        4.0f,</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        <span class="comment">// Batch 0, Channel 4, Height (1) x Width (1)</span></div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;        5.0f,</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        <span class="comment">// Batch 0, Channel 5, Height (1) x Width (1)</span></div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        6.0f,</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        <span class="comment">// Batch 0, Channel 6, Height (1) x Width (1)</span></div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        7.0f,</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        <span class="comment">// Batch 0, Channel 7, Height (1) x Width (1)</span></div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        8.0f,</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;        <span class="comment">// Batch 0, Channel 8, Height (1) x Width (1)</span></div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        9.0f,</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        <span class="comment">// Batch 0, Channel 9, Height (1) x Width (1)</span></div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        10.0f</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    };</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> approxInvL2Norm = 0.050964719f;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    {</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (1) x Width (1)</span></div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        1.0f * approxInvL2Norm,</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        2.0f * approxInvL2Norm,</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        3.0f * approxInvL2Norm,</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        4.0f * approxInvL2Norm,</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;        5.0f * approxInvL2Norm,</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;        6.0f * approxInvL2Norm,</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;        7.0f * approxInvL2Norm,</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        8.0f * approxInvL2Norm,</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;        9.0f * approxInvL2Norm,</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        10.0f * approxInvL2Norm</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    };</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    <span class="keywordflow">return</span> L2NormalizationTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        workloadFactory,</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;        memoryManager,</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;        inputOutputShape,</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        scale,</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        offset,</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;        inputValues,</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        outScale,</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        outOffset,</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        expectedOutputValues,</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        layout);</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;}</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> L2Normalization2dTestCommon(</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    int32_t offset,</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    int32_t outOffset,</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;{</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="comment">// Width: 5</span></div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    <span class="comment">// Height: 1</span></div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;    <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfBatches = 1;</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfChannels = 2;</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 1;</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 5;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a>(</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;            numberOfBatches, numberOfChannels, height, width, layout);</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    {</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (1) x Width (5)</span></div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;        1.0f, 3.0f, 5.0f, 7.0f,  9.0f,</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (1) x Width (5)</span></div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        2.0f, 4.0f, 6.0f, 8.0f, 10.0f</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    };</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    {</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (1) x Width (5)</span></div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;        1.0f * CalcInvL2Norm({ 1.0f,  2.0f }),</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        3.0f * CalcInvL2Norm({ 3.0f,  4.0f }),</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;        5.0f * CalcInvL2Norm({ 5.0f,  6.0f }),</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;        7.0f * CalcInvL2Norm({ 7.0f,  8.0f }),</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;        9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (1) x Width (5)</span></div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;        2.0f * CalcInvL2Norm({ 1.0f,  2.0f }),</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;        4.0f * CalcInvL2Norm({ 3.0f,  4.0f }),</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;        6.0f * CalcInvL2Norm({ 5.0f,  6.0f }),</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;        8.0f * CalcInvL2Norm({ 7.0f,  8.0f }),</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;        10.0f * CalcInvL2Norm({ 9.0f, 10.0f })</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    };</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    <span class="keywordflow">return</span> L2NormalizationTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        workloadFactory,</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;        memoryManager,</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;        inputOutputShape,</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;        scale,</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;        offset,</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;        inputValues,</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;        outScale,</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        outOffset,</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;        expectedOutputValues,</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        layout);</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;}</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> L2Normalization3dTestCommon(</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    int32_t offset,</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    int32_t outOffset,</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;{</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    <span class="comment">// Width: 3</span></div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    <span class="comment">// Height: 4</span></div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfBatches = 1;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfChannels = 2;</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 4;</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 3;</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a>(</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;            numberOfBatches, numberOfChannels, height, width, layout);</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    {</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (4) x Width (3)</span></div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;        119.0f,  21.0f, 150.0f,</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;        149.0f,  32.0f, 179.0f,</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;        15.0f, 227.0f, 141.0f,</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;        147.0f, 199.0f, 220.0f,</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (4) x Width (3)</span></div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;        110.0f, 140.0f,  73.0f,</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;        211.0f, 212.0f,  89.0f,</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;        24.0f, 138.0f, 188.0f,</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        162.0f,  12.0f, 161.0f</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    };</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    {</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (4) x Width (3)</span></div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;        119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;        21.0f * CalcInvL2Norm({  21.0f, 140.0f }),</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;        150.0f * CalcInvL2Norm({ 150.0f,  73.0f }),</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;        149.0f * CalcInvL2Norm({ 149.0f, 211.0f }),</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        32.0f * CalcInvL2Norm({  32.0f, 212.0f }),</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;        179.0f * CalcInvL2Norm({ 179.0f,  89.0f }),</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;        15.0f * CalcInvL2Norm({  15.0f,  24.0f }),</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;        227.0f * CalcInvL2Norm({ 227.0f, 138.0f }),</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;        141.0f * CalcInvL2Norm({ 141.0f, 188.0f }),</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;        147.0f * CalcInvL2Norm({ 147.0f, 162.0f }),</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;        199.0f * CalcInvL2Norm({ 199.0f,  12.0f }),</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;        220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (4) x Width (3)</span></div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;        110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;        140.0f * CalcInvL2Norm({  21.0f, 140.0f }),</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;        73.0f * CalcInvL2Norm({ 150.0f,  73.0f }),</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;        211.0f * CalcInvL2Norm({ 149.0f, 211.0f }),</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;        212.0f * CalcInvL2Norm({  32.0f, 212.0f }),</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;        89.0f * CalcInvL2Norm({ 179.0f,  89.0f }),</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;        24.0f * CalcInvL2Norm({  15.0f,  24.0f }),</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;        138.0f * CalcInvL2Norm({ 227.0f, 138.0f }),</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;        12.0f * CalcInvL2Norm({ 199.0f,  12.0f }),</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;        161.0f * CalcInvL2Norm({ 220.0f, 161.0f })</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    };</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="keywordflow">return</span> L2NormalizationTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;        workloadFactory,</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;        memoryManager,</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;        inputOutputShape,</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;        scale,</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;        offset,</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;        inputValues,</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        outScale,</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        outOffset,</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;        expectedOutputValues,</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;        layout);</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;}</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> L2Normalization4dTestCommon(</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    int32_t offset,</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    int32_t outOffset,</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;{</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    <span class="comment">// Width: 3</span></div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    <span class="comment">// Height: 4</span></div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    <span class="comment">// Channels: 3</span></div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    <span class="comment">// BatchSize: 2</span></div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfBatches = 2;</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfChannels = 3;</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 4;</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 3;</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape = <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a>(</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;            numberOfBatches, numberOfChannels, height, width, layout);</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    {</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (4) x Width (3)</span></div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;        235.0f,  46.0f, 178.0f,</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;        100.0f, 123.0f,  19.0f,</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;        172.0f,  74.0f, 250.0f,</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;        6.0f, 195.0f,  80.0f,</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (4) x Width (3)</span></div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;        113.0f,  95.0f, 202.0f,</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;        77.0f, 114.0f,  71.0f,</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;        122.0f, 246.0f, 166.0f,</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        82.0f,  28.0f,  37.0f,</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;        <span class="comment">// Batch 0, Channel 2, Height (4) x Width (3)</span></div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        56.0f, 170.0f, 162.0f,</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        194.0f,  89.0f, 254.0f,</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        12.0f, 209.0f, 200.0f,</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        1.0f,  64.0f,  54.0f,</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;        <span class="comment">// Batch 1, Channel 0, Height (4) x Width (3)</span></div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;        67.0f,  90.0f,  49.0f,</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        7.0f, 163.0f,  18.0f,</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        25.0f, 117.0f, 103.0f,</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        247.0f,  59.0f, 189.0f,</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        <span class="comment">// Batch 1, Channel 1, Height (4) x Width (3)</span></div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        239.0f, 104.0f, 199.0f,</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;        17.0f, 124.0f, 153.0f,</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;        222.0f, 217.0f, 75.0f,</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        32.0f, 126.0f, 21.0f,</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        <span class="comment">// Batch 1, Channel 2, Height (4) x Width (3)</span></div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;        97.0f, 145.0f, 215.0f,</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        115.0f, 116.0f, 238.0f,</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;        226.0f,  16.0f, 132.0f,</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;        92.0f, 125.0f,  88.0f</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;    };</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    {</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;        <span class="comment">// Batch 0, Channel 0, Height (4) x Width (3)</span></div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;        235.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;        46.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;        100.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;        123.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;        19.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;        172.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;        74.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;        250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;        6.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        195.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        80.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;        <span class="comment">// Batch 0, Channel 1, Height (4) x Width (3)</span></div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;        113.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;        95.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;        202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;        77.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;        114.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;        71.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;        122.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;        246.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;        82.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        28.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;        37.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;        <span class="comment">// Batch 0, Channel 2, Height (4) x Width (3)</span></div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;        56.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        170.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;        194.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        89.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        254.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;        12.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;        209.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;        200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;        1.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;        64.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;        54.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;        <span class="comment">// Batch 1, Channel 0, Height (4) x Width (3)</span></div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;        67.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;        90.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;        49.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;        7.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;        163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;        18.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;        25.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;        117.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;        103.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        247.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;        59.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;        189.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f }),</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;        <span class="comment">// Batch 1, Channel 1, Height (4) x Width (3)</span></div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;        239.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;        104.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;        199.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;        17.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;        124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;        153.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;        222.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;        217.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;        75.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;        32.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;        126.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;        21.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f }),</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;        <span class="comment">// Batch 1, Channel 2, Height (4) x Width (3)</span></div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;        97.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;        145.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;        215.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;        115.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;        238.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        226.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;        16.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;        132.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;        92.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;        125.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;        88.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f })</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    };</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;    <span class="keywordflow">return</span> L2NormalizationTestImpl&lt;ArmnnType&gt;(</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;        workloadFactory,</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;        memoryManager,</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;        inputOutputShape,</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;        scale,</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;        offset,</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;        inputValues,</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;        outScale,</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;        outOffset,</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;        expectedOutputValues,</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;        layout);</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;}</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;} <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;</div><div class="line"><a name="l00547"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a13c8cd6115422815348d57aef2ca032d">  547</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a13c8cd6115422815348d57aef2ca032d">L2NormalizationDefaultEpsilonTest</a>(</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;        <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;{</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    <span class="comment">// Dummy descriptor to get the default value of epsilon.</span></div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;    <span class="keywordflow">return</span> L2NormalizationEpsilonTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;        workloadFactory,</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;        memoryManager,</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;        0.f,</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;        0,</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;        0.f,</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;        0,</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;        layout,</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;        descriptor.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a>);</div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;}</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;</div><div class="line"><a name="l00566"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#ae6ec1c0ad5b1b94d03c160c8122587cc">  566</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#ae6ec1c0ad5b1b94d03c160c8122587cc">L2NormalizationNonDefaultEpsilonTest</a>(</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;        <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;{</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;    <span class="keywordflow">return</span> L2NormalizationEpsilonTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;        workloadFactory,</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;        memoryManager,</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;        0.f,</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;        0,</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;        0.f,</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;        0,</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;        layout,</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;        1e-9f);</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;}</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;</div><div class="line"><a name="l00582"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#abc9aa62ee9cdec8c43b5a43d931c632c">  582</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#abc9aa62ee9cdec8c43b5a43d931c632c">L2Normalization1dTest</a>(</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;{</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;        workloadFactory,</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;        memoryManager,</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;        0.f,</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;        0,</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;        0.f,</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;        0,</div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;        layout);</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;}</div><div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;</div><div class="line"><a name="l00597"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a51324dd32b0b605e9f27d2b91312dc80">  597</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a51324dd32b0b605e9f27d2b91312dc80">L2Normalization1dInt16Test</a>(</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;{</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::QSymmS16&gt;(</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;        workloadFactory,</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;        memoryManager,</div><div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;        1.f,</div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;        0,</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;        1.f,</div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;        0,</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;        layout);</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;}</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;</div><div class="line"><a name="l00612"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#aea6a1743ba0fdb000d73856302ab6c23">  612</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#aea6a1743ba0fdb000d73856302ab6c23">L2Normalization1dUint8Test</a>(</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;{</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;        workloadFactory,</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;        memoryManager,</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;        1.f,</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;        0,</div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;        1.f / 128,</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;        128,</div><div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;        layout);</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;}</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;</div><div class="line"><a name="l00627"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a373fc44a34b2bba8739ad4c6e864b234">  627</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a373fc44a34b2bba8739ad4c6e864b234">L2Normalization2dTest</a>(</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;{</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;    <span class="keywordflow">return</span> L2Normalization2dTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;        workloadFactory,</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;        memoryManager,</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;        0.f,</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;        0,</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;        0.f,</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;        0,</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;        layout);</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;}</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;</div><div class="line"><a name="l00642"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a6e2879224854a663f502d3092a68d2c7">  642</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a6e2879224854a663f502d3092a68d2c7">L2Normalization2dInt16Test</a>(</div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;{</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::QSymmS16&gt;(</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;        workloadFactory,</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;        memoryManager,</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;        1.f,</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;        0,</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;        1.f,</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;        0,</div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;        layout);</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;}</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;</div><div class="line"><a name="l00657"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a96cf65cb33a0e9319ddd0d00d56b5056">  657</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a96cf65cb33a0e9319ddd0d00d56b5056">L2Normalization2dUint8Test</a>(</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;{</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;        workloadFactory,</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;        memoryManager,</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;        1.f,</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;        0,</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;        1.f / 128,</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;        128,</div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;        layout);</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;}</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;</div><div class="line"><a name="l00672"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a1a90f524b460439bb8e386ea672acd6c">  672</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 2&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a1a90f524b460439bb8e386ea672acd6c">L2Normalization2dShapeTest</a>(</div><div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;{</div><div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputTensorShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>({ 5, 2 });</div><div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;    std::vector&lt;float&gt; inputData</div><div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;    {</div><div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;        1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f</div><div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;    };</div><div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;    std::vector&lt;float&gt; expectedOutputData</div><div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;    {</div><div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;        1.0f * CalcInvL2Norm({ 1.0f,  2.0f }),</div><div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;        2.0f * CalcInvL2Norm({ 1.0f,  2.0f }),</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;        3.0f * CalcInvL2Norm({ 3.0f,  4.0f }),</div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;        4.0f * CalcInvL2Norm({ 3.0f,  4.0f }),</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;        5.0f * CalcInvL2Norm({ 5.0f,  6.0f }),</div><div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;        6.0f * CalcInvL2Norm({ 5.0f,  6.0f }),</div><div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;        7.0f * CalcInvL2Norm({ 7.0f,  8.0f }),</div><div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;        8.0f * CalcInvL2Norm({ 7.0f,  8.0f }),</div><div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;        9.0f  * CalcInvL2Norm({ 9.0f, 10.0f }),</div><div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;        10.0f * CalcInvL2Norm({ 9.0f, 10.0f })</div><div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;    };</div><div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;</div><div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(inputOutputTensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.f, 0);</div><div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(inputOutputTensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.f, 0);</div><div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;</div><div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;    <span class="keyword">auto</span> inputTensor = MakeTensor&lt;float, 2&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;</div><div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;    <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 2&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;    result.outputExpected = MakeTensor&lt;float, 2&gt;(outputTensorInfo, expectedOutputData);</div><div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;</div><div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;</div><div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;    <a class="code" href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml">armnn::L2NormalizationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 1e-12f;</div><div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160; 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   <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;}</div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;</div><div class="line"><a name="l00731"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a336e63cb246a1d6f8b5a02367932471a">  731</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a336e63cb246a1d6f8b5a02367932471a">L2Normalization3dTest</a>(</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;{</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    <span class="keywordflow">return</span> L2Normalization3dTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;        workloadFactory,</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;        memoryManager,</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;        0.f,</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;        0,</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;        0.f,</div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;        0,</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;        layout);</div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;}</div><div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;</div><div class="line"><a name="l00746"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a49295d2552ff6a80396649f5b6e3a9ce">  746</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a49295d2552ff6a80396649f5b6e3a9ce">L2Normalization3dInt16Test</a>(</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160; 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       1.f,</div><div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;        0,</div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;        1.f,</div><div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;        0,</div><div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;        layout);</div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;}</div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;</div><div class="line"><a name="l00761"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#ad087db636160f71155a4ac31b37184aa">  761</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#ad087db636160f71155a4ac31b37184aa">L2Normalization3dUint8Test</a>(</div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;{</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;        workloadFactory,</div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;        memoryManager,</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;        1.f,</div><div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;        0,</div><div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;        1.f / 128,</div><div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;        128,</div><div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;        layout);</div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;}</div><div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;</div><div class="line"><a name="l00776"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a49c8b464589cbe8f6b7e7a1bf7e6403d">  776</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a49c8b464589cbe8f6b7e7a1bf7e6403d">L2Normalization4dTest</a>(</div><div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;{</div><div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;    <span class="keywordflow">return</span> L2Normalization4dTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;        workloadFactory,</div><div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;        memoryManager,</div><div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;        0.f,</div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;        0,</div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;        0.f,</div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;        0,</div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;        layout);</div><div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;}</div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;</div><div class="line"><a name="l00791"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a1bbff607f046d49a92516969d8beff7a">  791</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a1bbff607f046d49a92516969d8beff7a">L2Normalization4dInt16Test</a>(</div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;{</div><div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::QSymmS16&gt;(</div><div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;        workloadFactory,</div><div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;        memoryManager,</div><div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160;        1.f,</div><div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;        0,</div><div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160;        1.f,</div><div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;        0,</div><div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;        layout);</div><div class="line"><a name="l00804"></a><span class="lineno">  804</span>&#160;}</div><div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;</div><div class="line"><a name="l00806"></a><span class="lineno"><a class="line" href="_l2_normalization_test_impl_8hpp.xhtml#a9672054d1096864d4c034aa90008efff">  806</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_l2_normalization_test_impl_8cpp.xhtml#a9672054d1096864d4c034aa90008efff">L2Normalization4dUint8Test</a>(</div><div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160;{</div><div class="line"><a name="l00811"></a><span class="lineno">  811</span>&#160;    <span class="keywordflow">return</span> L2Normalization1dTestCommon&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00812"></a><span class="lineno">  812</span>&#160;        workloadFactory,</div><div class="line"><a name="l00813"></a><span class="lineno">  813</span>&#160;        memoryManager,</div><div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;        1.f,</div><div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;        0,</div><div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;        1.f / 128,</div><div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;        128,</div><div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;        layout);</div><div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::L2NormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00604">Descriptors.hpp:604</a></div></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_a3c86f886e36ce943f1ebc241a37f0413"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a3c86f886e36ce943f1ebc241a37f0413">armnn::IWorkloadFactory::CreateL2Normalization</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateL2Normalization(const L2NormalizationQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01250">WorkloadFactory.cpp:1250</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00049">Types.hpp:49</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="_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#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="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_abc9aa62ee9cdec8c43b5a43d931c632c"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#abc9aa62ee9cdec8c43b5a43d931c632c">L2Normalization1dTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; L2Normalization1dTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00582">L2NormalizationTestImpl.cpp:582</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 &amp;&amp;...)</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="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</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="_l2_normalization_test_impl_8cpp_xhtml_ad087db636160f71155a4ac31b37184aa"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#ad087db636160f71155a4ac31b37184aa">L2Normalization3dUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; L2Normalization3dUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00761">L2NormalizationTestImpl.cpp:761</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a9672054d1096864d4c034aa90008efff"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a9672054d1096864d4c034aa90008efff">L2Normalization4dUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; L2Normalization4dUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00806">L2NormalizationTestImpl.cpp:806</a></div></div>
<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::L2NormalizationDescriptor::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#l00606">Descriptors.hpp:606</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a373fc44a34b2bba8739ad4c6e864b234"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a373fc44a34b2bba8739ad4c6e864b234">L2Normalization2dTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; L2Normalization2dTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00627">L2NormalizationTestImpl.cpp:627</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a13c8cd6115422815348d57aef2ca032d"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a13c8cd6115422815348d57aef2ca032d">L2NormalizationDefaultEpsilonTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; L2NormalizationDefaultEpsilonTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00547">L2NormalizationTestImpl.cpp:547</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</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="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a></div><div class="ttdoc">A L2NormalizationDescriptor for the L2NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</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&lt; IMemoryManager &gt; 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="_l2_normalization_test_impl_8cpp_xhtml_a6e2879224854a663f502d3092a68d2c7"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a6e2879224854a663f502d3092a68d2c7">L2Normalization2dInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; L2Normalization2dInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00642">L2NormalizationTestImpl.cpp:642</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a49295d2552ff6a80396649f5b6e3a9ce"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a49295d2552ff6a80396649f5b6e3a9ce">L2Normalization3dInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; L2Normalization3dInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00746">L2NormalizationTestImpl.cpp:746</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a></div><div class="ttdeci">armnn::TensorShape GetTensorShape(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00019">TensorUtils.cpp:19</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_aea6a1743ba0fdb000d73856302ab6c23"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#aea6a1743ba0fdb000d73856302ab6c23">L2Normalization1dUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; L2Normalization1dUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00612">L2NormalizationTestImpl.cpp:612</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_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00173">Types.hpp:173</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a96cf65cb33a0e9319ddd0d00d56b5056"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a96cf65cb33a0e9319ddd0d00d56b5056">L2Normalization2dUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; L2Normalization2dUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00657">L2NormalizationTestImpl.cpp:657</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&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
<div class="ttc" id="_l2_normalization_test_impl_8hpp_xhtml"><div class="ttname"><a href="_l2_normalization_test_impl_8hpp.xhtml">L2NormalizationTestImpl.hpp</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a1a90f524b460439bb8e386ea672acd6c"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a1a90f524b460439bb8e386ea672acd6c">L2Normalization2dShapeTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 2 &gt; L2Normalization2dShapeTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00672">L2NormalizationTestImpl.cpp:672</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a1bbff607f046d49a92516969d8beff7a"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a1bbff607f046d49a92516969d8beff7a">L2Normalization4dInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; L2Normalization4dInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00791">L2NormalizationTestImpl.cpp:791</a></div></div>
<div class="ttc" id="_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_utils_8hpp.xhtml">TensorUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_l2_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml">armnn::L2NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00316">WorkloadData.hpp:316</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="_l2_normalization_test_impl_8cpp_xhtml_ae6ec1c0ad5b1b94d03c160c8122587cc"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#ae6ec1c0ad5b1b94d03c160c8122587cc">L2NormalizationNonDefaultEpsilonTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; L2NormalizationNonDefaultEpsilonTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00566">L2NormalizationTestImpl.cpp:566</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#l00029">LayerTestResult.hpp:29</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a336e63cb246a1d6f8b5a02367932471a"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a336e63cb246a1d6f8b5a02367932471a">L2Normalization3dTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; L2Normalization3dTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00731">L2NormalizationTestImpl.cpp:731</a></div></div>
<div class="ttc" id="_l2_normalization_test_impl_8cpp_xhtml_a49c8b464589cbe8f6b7e7a1bf7e6403d"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a49c8b464589cbe8f6b7e7a1bf7e6403d">L2Normalization4dTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; L2Normalization4dTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00776">L2NormalizationTestImpl.cpp:776</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="_l2_normalization_test_impl_8cpp_xhtml_a51324dd32b0b605e9f27d2b91312dc80"><div class="ttname"><a href="_l2_normalization_test_impl_8cpp.xhtml#a51324dd32b0b605e9f27d2b91312dc80">L2Normalization1dInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; L2Normalization1dInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_test_impl_8cpp_source.xhtml#l00597">L2NormalizationTestImpl.cpp:597</a></div></div>
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