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<div class="title">BatchNormalizationTestImpl.cpp</div> </div>
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<a href="_batch_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="_batch_normalization_test_impl_8hpp.xhtml">BatchNormalizationTestImpl.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="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.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="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.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="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.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="include_2armnn_2backends_2_i_backend_internal_8hpp.xhtml">armnn/backends/IBackendInternal.hpp</a>&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_factory_8hpp.xhtml">backendsCommon/WorkloadFactory.hpp</a>&gt;</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<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="l00019"></a><span class="lineno"> 19</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="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</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="l00022"></a><span class="lineno"> 22</span>&#160;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;{</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</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="l00029"></a><span class="lineno"> 29</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> BatchNormTestImpl(</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</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="l00032"></a><span class="lineno"> 32</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="l00033"></a><span class="lineno"> 33</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; inputValues,</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; expectedOutputValues,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; int32_t qOffset,</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;{</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(inputOutputTensorShape, ArmnnType);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(inputOutputTensorShape, ArmnnType);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> dataLayoutIndexed(dataLayout);</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; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({ inputOutputTensorShape[dataLayoutIndexed.GetChannelsIndex()] },</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; ArmnnType);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="keywordflow">if</span> (armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; outputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; outputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; tensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; tensorInfo.SetQuantizationOffset(qOffset);</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;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="keyword">auto</span> inputTensor = MakeTensor&lt;T, 4&gt;(inputTensorInfo, QuantizedVector&lt;T&gt;(inputValues, qScale, qOffset));</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="comment">// These values are per-channel of the input.</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="keyword">auto</span> mean = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 3, -2 }, qScale, qOffset));</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="keyword">auto</span> variance = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 4, 9 }, qScale, qOffset));</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="keyword">auto</span> beta = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 3, 2 }, qScale, qOffset));</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keyword">auto</span> gamma = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 2, 1 }, qScale, qOffset));</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; result.outputExpected = MakeTensor&lt;T, 4&gt;(inputTensorInfo,</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; QuantizedVector&lt;T&gt;(expectedOutputValues, qScale, qOffset));</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</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="l00073"></a><span class="lineno"> 73</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="l00074"></a><span class="lineno"> 74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> meanTensor(tensorInfo);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> varianceTensor(tensorInfo);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> betaTensor(tensorInfo);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> gammaTensor(tensorInfo);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">m_Mean</a> = &amp;meanTensor;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">m_Variance</a> = &amp;varianceTensor;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">m_Beta</a> = &amp;betaTensor;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">m_Gamma</a> = &amp;gammaTensor;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</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_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.0f;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</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_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</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="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;meanTensor, &amp;mean[0]);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;varianceTensor, &amp;variance[0]);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;betaTensor, &amp;beta[0]);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;gammaTensor, &amp;gamma[0]);</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; AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());</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; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">CreateBatchNormalization</a>(descriptor, info);</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; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</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="l00103"></a><span class="lineno"> 103</span>&#160;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</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="l00107"></a><span class="lineno"> 107</span>&#160;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160;}</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</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="l00112"></a><span class="lineno"> 112</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T,4&gt;</a> BatchNormTestNhwcImpl(</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</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="l00115"></a><span class="lineno"> 115</span>&#160; <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; int32_t qOffset)</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;{</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 2;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 3;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 2;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num = 1;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({num, height, width, channels}, ArmnnType);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({num, height, width, channels}, ArmnnType);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({channels}, ArmnnType);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; outputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; outputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; tensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; tensorInfo.SetQuantizationOffset(qOffset);</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">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; QuantizedVector&lt;T&gt;(</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; 1.f, 1.f, 4.f, 1.f,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; 4.f, 4.f, 2.f, 1.f,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; 1.f, -2.f, 6.f, 4.f</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; },</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; <span class="comment">// These values are per-channel of the input.</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; <span class="keyword">auto</span> mean = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 3, -2 }, qScale, qOffset));</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="keyword">auto</span> variance = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 4, 9 }, qScale, qOffset));</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <span class="keyword">auto</span> beta = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 3, 2 }, qScale, qOffset));</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="keyword">auto</span> gamma = MakeTensor&lt;T, 1&gt;(tensorInfo, QuantizedVector&lt;T&gt;({ 2, 1 }, qScale, qOffset));</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T,4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</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="l00156"></a><span class="lineno"> 156</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="l00157"></a><span class="lineno"> 157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a> data;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> meanTensor(tensorInfo);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> varianceTensor(tensorInfo);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> betaTensor(tensorInfo);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> gammaTensor(tensorInfo);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;meanTensor, &amp;mean[0]);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;varianceTensor, &amp;variance[0]);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;betaTensor, &amp;beta[0]);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;gammaTensor, &amp;gamma[0]);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">m_Mean</a> = &amp;meanTensor;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">m_Variance</a> = &amp;varianceTensor;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">m_Beta</a> = &amp;betaTensor;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">m_Gamma</a> = &amp;gammaTensor;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.0f;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <span class="comment">// For each channel:</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <span class="comment">// substract mean, divide by standard deviation (with an epsilon to avoid div by 0),</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <span class="comment">// multiply by gamma and add beta</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; ret.outputExpected = MakeTensor&lt;T, 4&gt;(outputTensorInfo,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; 1.f, 3.f, 4.f, 3.f,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; 4.f, 4.f, 2.f, 3.f,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; 1.f, 2.f, 6.f, 4.f</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; },</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; qScale, qOffset));</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; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">CreateBatchNormalization</a>(data, info);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; workload-&gt;Execute();</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; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160;}</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160;} <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160;</div><div class="line"><a name="l00207"></a><span class="lineno"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#a95e3411d80e0eac3844844c017f03861"> 207</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#a95e3411d80e0eac3844844c017f03861">BatchNormFloat32Test</a>(</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</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="l00210"></a><span class="lineno"> 210</span>&#160;{</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; <span class="comment">// Width: 2</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; 4.f, 1.f,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; -2.f, 4.f</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; };</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160;</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; 3.f, 3.f,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; 4.f, 3.f,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; 2.f, 4.f</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; };</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; workloadFactory,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; memoryManager,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; inputOutputShape,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; inputValues,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; expectedOutputValues,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; 0.f,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; 0,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160;}</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160;</div><div class="line"><a name="l00253"></a><span class="lineno"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#a449a360cd864483064ae2991db8edcd8"> 253</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#a449a360cd864483064ae2991db8edcd8">BatchNormFloat32NhwcTest</a>(</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</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="l00256"></a><span class="lineno"> 256</span>&#160;{</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; <span class="comment">// Width: 2</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;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; 4.f, 1.f,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160;</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; 2.f, 1.f,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; 1.f, -2.f,</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; 6.f, 4.f</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, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; 1.f, 3.f,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; 4.f, 3.f,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; 2.f, 3.f,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; 1.f, 2.f,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; 6.f, 4.f</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; };</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; workloadFactory,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; memoryManager,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; inputOutputShape,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; inputValues,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; expectedOutputValues,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; 0.f,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; 0,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160;}</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160;</div><div class="line"><a name="l00303"></a><span class="lineno"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#a0fe6b55e33196820f9bf4759647c17df"> 303</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;armnn::Half, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#a0fe6b55e33196820f9bf4759647c17df">BatchNormFloat16Test</a>(</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</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="l00306"></a><span class="lineno"> 306</span>&#160;{</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; <span class="comment">// Width: 2</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160;</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; 4.f, 1.f,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; -2.f, 4.f</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; };</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; {</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; 3.f, 3.f,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; 4.f, 3.f,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; 2.f, 4.f</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; };</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::Float16&gt;(</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; workloadFactory,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; memoryManager,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; inputOutputShape,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; inputValues,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; expectedOutputValues,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; 0.f,</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; 0,</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160;}</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160;</div><div class="line"><a name="l00349"></a><span class="lineno"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#a7615443ac0887d4c282f53f7e49d889c"> 349</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;armnn::Half, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#a7615443ac0887d4c282f53f7e49d889c">BatchNormFloat16NhwcTest</a>(</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</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="l00352"></a><span class="lineno"> 352</span>&#160;{</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; <span class="comment">// Width: 2</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; <span class="comment">// Channels: 2</span></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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; 4.f, 1.f,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; 2.f, 1.f,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160;</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; 1.f, -2.f,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; 6.f, 4.f</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; std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; 1.f, 3.f,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; 4.f, 3.f,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; 2.f, 3.f,</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160;</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; 1.f, 2.f,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; 6.f, 4.f</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; };</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::Float16&gt;(</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; workloadFactory,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; memoryManager,</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; inputOutputShape,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; inputValues,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; expectedOutputValues,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; 0.f,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; 0,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160;}</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160;</div><div class="line"><a name="l00399"></a><span class="lineno"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#ae90e750efd98b6fb3db4bd586df3daff"> 399</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#ae90e750efd98b6fb3db4bd586df3daff">BatchNormUint8Test</a>(</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</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="l00402"></a><span class="lineno"> 402</span>&#160;{</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; <span class="comment">// Width: 2</span></div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; {</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160; 4.f, 1.f,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; -2.f, 4.f</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; std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160; <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160;</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; 3.f, 3.f,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; 4.f, 3.f,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; 2.f, 4.f</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;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; workloadFactory,</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; memoryManager,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; inputOutputShape,</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; inputValues,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; expectedOutputValues,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; 1.f / 20.f,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; 50,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160;}</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"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#a168bb6829b7b1bd091ab3800a055f7ee"> 445</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#a168bb6829b7b1bd091ab3800a055f7ee">BatchNormUint8NhwcTest</a>(</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</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="l00448"></a><span class="lineno"> 448</span>&#160;{</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; <span class="comment">// Width: 2</span></div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160;</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160; <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160; 4.f, 1.f,</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, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; 2.f, 1.f,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; 1.f, -2.f,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; 6.f, 4.f</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; };</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; 1.f, 3.f,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; 4.f, 3.f,</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, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; 2.f, 3.f,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160;</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; 1.f, 2.f,</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; 6.f, 4.f</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; };</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160;</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; workloadFactory,</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; memoryManager,</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; inputOutputShape, inputValues, expectedOutputValues,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; 1.f/20.f, 50, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160;}</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160;</div><div class="line"><a name="l00491"></a><span class="lineno"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#aa3fcd011e2fba798b1d5c8d4d2ee9ad8"> 491</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#aa3fcd011e2fba798b1d5c8d4d2ee9ad8">BatchNormInt16Test</a>(</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</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="l00494"></a><span class="lineno"> 494</span>&#160;{</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; <span class="comment">// Width: 2</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; std::vector&lt;float&gt; inputValues</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 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; 4.f, 1.f,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; -2.f, 4.f</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160; };</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160; {</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160; <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; 1.f, 4.f,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; 4.f, 2.f,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160; 1.f, 6.f,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160;</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; 3.f, 3.f,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; 4.f, 3.f,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; 2.f, 4.f</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; };</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::QSymmS16&gt;(</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; workloadFactory,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160; memoryManager,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; inputOutputShape,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; inputValues,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; expectedOutputValues,</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; 1.f / 20.f,</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; 50,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160;}</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160;</div><div class="line"><a name="l00537"></a><span class="lineno"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#a40379f76fb69d26e8543dd1494674335"> 537</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#a40379f76fb69d26e8543dd1494674335">BatchNormInt16NhwcTest</a>(</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</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="l00540"></a><span class="lineno"> 540</span>&#160;{</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160; <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160; <span class="comment">// Height: 3</span></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>&#160; <span class="comment">// Width: 2</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>&#160; <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>&#160;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160; std::vector&lt;float&gt; inputValues</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; {</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160; <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160; 1.f, 1.f,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160; 4.f, 1.f,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160; <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160; 2.f, 1.f,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160;</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160; <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160; 1.f, -2.f,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160; 6.f, 4.f</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; };</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160; std::vector&lt;float&gt; expectedOutputValues</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>&#160; {</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160; 1.f, 3.f,</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160; 4.f, 3.f,</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160;</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>&#160; <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>&#160; 4.f, 4.f,</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>&#160; 2.f, 3.f,</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="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>&#160; 1.f, 2.f,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>&#160; 6.f, 4.f</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>&#160; };</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160;</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160; <span class="keywordflow">return</span> BatchNormTestImpl&lt;armnn::DataType::QSymmS16&gt;(</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160; workloadFactory,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160; memoryManager,</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160; inputOutputShape,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>&#160; inputValues,</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>&#160; expectedOutputValues,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>&#160; 1.f / 20.f,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>&#160; 50,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>&#160;}</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"><a class="line" href="_batch_normalization_test_impl_8hpp.xhtml#a3d152f106fe6d057602fdb6271a2046a"> 587</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,4&gt;</a> <a class="code" href="_batch_normalization_test_impl_8cpp.xhtml#a39988d3dc5c636fa49e8192f26d72554">CompareBatchNormTest</a>(</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</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="l00590"></a><span class="lineno"> 590</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; refWorkloadFactory)</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>&#160;{</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 2;</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 3;</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 5;</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 3;</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>&#160;</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo;</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; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {batchSize, channels, height, width};</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorShape[] = {channels};</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>&#160;</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>&#160; inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>&#160; outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>&#160; tensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, tensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>&#160;</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>&#160; <span class="keyword">auto</span> input = MakeRandomTensor&lt;float, 4&gt;(inputTensorInfo, 21312);</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; <span class="keyword">auto</span> mean = MakeRandomTensor&lt;float, 1&gt;(tensorInfo, 123);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>&#160; <span class="keyword">auto</span> variance = MakeRandomTensor&lt;float, 1&gt;(tensorInfo, 234, 0.0f);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>&#160; <span class="keyword">auto</span> beta = MakeRandomTensor&lt;float, 1&gt;(tensorInfo, 123);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>&#160; <span class="keyword">auto</span> gamma = MakeRandomTensor&lt;float, 1&gt;(tensorInfo, 345);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>&#160;</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>&#160;</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</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="l00619"></a><span class="lineno"> 619</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="l00620"></a><span class="lineno"> 620</span>&#160;</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>&#160;</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>&#160; <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a> data;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> meanTensor(tensorInfo);</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> varianceTensor(tensorInfo);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> betaTensor(tensorInfo);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>&#160; <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> gammaTensor(tensorInfo);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>&#160;</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;meanTensor, &amp;mean[0]);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;varianceTensor, &amp;variance[0]);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;betaTensor, &amp;beta[0]);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&amp;gammaTensor, &amp;gamma[0]);</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>&#160;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">m_Mean</a> = &amp;meanTensor;</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">m_Variance</a> = &amp;varianceTensor;</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">m_Beta</a> = &amp;betaTensor;</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>&#160; data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">m_Gamma</a> = &amp;gammaTensor;</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.01f;</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>&#160;</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>&#160; <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>&#160; SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>&#160; SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>&#160;</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">CreateBatchNormalization</a>(data, info);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">CreateBatchNormalization</a>(refData, refInfo);</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>&#160;</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>&#160; inputHandleRef-&gt;Allocate();</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>&#160; outputHandleRef-&gt;Allocate();</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"> 657</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>&#160;</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>&#160; workloadRef-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>&#160; workloadRef-&gt;Execute();</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>&#160;</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.outputExpected[0][0][0][0], outputHandleRef.get());</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>&#160;</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_abe1e0d40e23195022c0bc10a8aab55ea"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">armnn::IWorkloadFactory::CreateBatchNormalization</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateBatchNormalization(const BatchNormalizationQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01117">WorkloadFactory.cpp:1117</a></div></div>
<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</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="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_afbe59e02a5464703b865ea1ccfff49fd"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">armnn::BatchNormalizationQueueDescriptor::m_Gamma</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Gamma</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00282">WorkloadData.hpp:282</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_ad5f8f205ba69eb186688ca1c2aec207c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">armnn::BatchNormalizationQueueDescriptor::m_Beta</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Beta</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00281">WorkloadData.hpp:281</a></div></div>
<div class="ttc" id="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</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="_batch_normalization_test_impl_8cpp_xhtml_aa3fcd011e2fba798b1d5c8d4d2ee9ad8"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#aa3fcd011e2fba798b1d5c8d4d2ee9ad8">BatchNormInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; BatchNormInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00491">BatchNormalizationTestImpl.cpp:491</a></div></div>
<div class="ttc" id="_workload_factory_8hpp_xhtml"><div class="ttname"><a href="_workload_factory_8hpp.xhtml">WorkloadFactory.hpp</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="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a40051a7aa82f25df43cc4244de04a7ec"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">armnn::BatchNormalizationQueueDescriptor::m_Mean</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Mean</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00279">WorkloadData.hpp:279</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00623">Descriptors.hpp:623</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchNormalizationDescriptor::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#l00625">Descriptors.hpp:625</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a8cd8696bb773a02714d3fc095794ec54"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">armnn::BatchNormalizationQueueDescriptor::m_Variance</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Variance</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00280">WorkloadData.hpp:280</a></div></div>
<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_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="_batch_normalization_test_impl_8cpp_xhtml_ae90e750efd98b6fb3db4bd586df3daff"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#ae90e750efd98b6fb3db4bd586df3daff">BatchNormUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; BatchNormUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00399">BatchNormalizationTestImpl.cpp:399</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="_batch_normalization_test_impl_8cpp_xhtml_a168bb6829b7b1bd091ab3800a055f7ee"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#a168bb6829b7b1bd091ab3800a055f7ee">BatchNormUint8NhwcTest</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; BatchNormUint8NhwcTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00445">BatchNormalizationTestImpl.cpp:445</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="include_2armnn_2backends_2_i_backend_internal_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_2backends_2_i_backend_internal_8hpp.xhtml">IBackendInternal.hpp</a></div></div>
<div class="ttc" id="_batch_normalization_test_impl_8cpp_xhtml_a449a360cd864483064ae2991db8edcd8"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#a449a360cd864483064ae2991db8edcd8">BatchNormFloat32NhwcTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; BatchNormFloat32NhwcTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00253">BatchNormalizationTestImpl.cpp:253</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="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div>
<div class="ttc" id="_batch_normalization_test_impl_8cpp_xhtml_a7615443ac0887d4c282f53f7e49d889c"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#a7615443ac0887d4c282f53f7e49d889c">BatchNormFloat16NhwcTest</a></div><div class="ttdeci">LayerTestResult&lt; armnn::Half, 4 &gt; BatchNormFloat16NhwcTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00349">BatchNormalizationTestImpl.cpp:349</a></div></div>
<div class="ttc" id="_batch_normalization_test_impl_8cpp_xhtml_a40379f76fb69d26e8543dd1494674335"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#a40379f76fb69d26e8543dd1494674335">BatchNormInt16NhwcTest</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; BatchNormInt16NhwcTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00537">BatchNormalizationTestImpl.cpp:537</a></div></div>
<div class="ttc" id="_batch_normalization_test_impl_8cpp_xhtml_a0fe6b55e33196820f9bf4759647c17df"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#a0fe6b55e33196820f9bf4759647c17df">BatchNormFloat16Test</a></div><div class="ttdeci">LayerTestResult&lt; armnn::Half, 4 &gt; BatchNormFloat16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00303">BatchNormalizationTestImpl.cpp:303</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00259">Tensor.cpp:259</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
<div class="ttc" id="_batch_normalization_test_impl_8hpp_xhtml"><div class="ttname"><a href="_batch_normalization_test_impl_8hpp.xhtml">BatchNormalizationTestImpl.hpp</a></div></div>
<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00269">WorkloadData.hpp:269</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00013">DataLayoutIndexed.hpp:13</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="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
<div class="ttc" id="_batch_normalization_test_impl_8cpp_xhtml_a39988d3dc5c636fa49e8192f26d72554"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#a39988d3dc5c636fa49e8192f26d72554">CompareBatchNormTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; CompareBatchNormTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::IWorkloadFactory &amp;refWorkloadFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00587">BatchNormalizationTestImpl.cpp:587</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="_batch_normalization_test_impl_8cpp_xhtml_a95e3411d80e0eac3844844c017f03861"><div class="ttname"><a href="_batch_normalization_test_impl_8cpp.xhtml#a95e3411d80e0eac3844844c017f03861">BatchNormFloat32Test</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; BatchNormFloat32Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_test_impl_8cpp_source.xhtml#l00207">BatchNormalizationTestImpl.cpp:207</a></div></div>
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