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<div class="title">InstanceNormalizationEndToEndTestImpl.cpp</div> </div>
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<a href="_instance_normalization_end_to_end_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 © 2019 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="_instance_normalization_end_to_end_test_impl_8hpp.xhtml">InstanceNormalizationEndToEndTestImpl.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_end_to_end_test_impl_8hpp.xhtml">EndToEndTestImpl.hpp</a>&quot;</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</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="l00012"></a><span class="lineno"> 12</span>&#160;</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_data_layout_utils_8hpp.xhtml">backendsCommon/test/DataLayoutUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_test_utils_8hpp.xhtml">test/TestUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="preprocessor">#include &lt;boost/test/unit_test.hpp&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="keyword">namespace</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;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> armnn::DataType DataType&gt;</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateInstanceNormalizationNetwork(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>&amp; inputShape,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>&amp; outputShape,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> gamma,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> eps,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keyword">const</span> int32_t qOffset = 0)</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;{</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">INetwork::Create</a>());</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="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml">InstanceNormalizationDescriptor</a> instanceNormalizationDesc;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; instanceNormalizationDesc.<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">m_Gamma</a> = gamma;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; instanceNormalizationDesc.<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = beta;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; instanceNormalizationDesc.<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = eps;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; instanceNormalizationDesc.<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* instanceNormalization = net-&gt;AddInstanceNormalizationLayer(instanceNormalizationDesc,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="stringliteral">&quot;InstanceNormalization&quot;</span>);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net-&gt;AddInputLayer(0, <span class="stringliteral">&quot;input&quot;</span>);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, instanceNormalization, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net-&gt;AddOutputLayer(0, <span class="stringliteral">&quot;output&quot;</span>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(instanceNormalization, output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="keywordflow">return</span> net;</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="keywordtype">void</span> InstanceNormalizationEndToEnd(<span class="keyword">const</span> std::vector&lt;armnn::BackendId&gt;&amp; backends,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>&amp; dataLayout,</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; inputTensorInfo,</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; outputTensorInfo,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; std::vector&lt;float&gt;&amp; inputData,</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; std::vector&lt;float&gt;&amp; expectedOutputData,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> gamma,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> eps)</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; <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; PermuteTensorNhwcToNchw&lt;float&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; PermuteTensorNhwcToNchw&lt;float&gt;(outputTensorInfo, expectedOutputData);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; }</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateInstanceNormalizationNetwork&lt;DataType::Float32&gt;(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; dataLayout,</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; gamma,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; beta,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; eps);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; BOOST_TEST_CHECKPOINT(<span class="stringliteral">&quot;Create a network&quot;</span>);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; std::map&lt;int, std::vector&lt;float&gt;&gt; inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; std::map&lt;int, std::vector&lt;float&gt;&gt; expectedOutputTensorData = { { 0, expectedOutputData } };</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; EndToEndLayerTestImpl&lt;DataType::Float32, DataType::Float32&gt;(move(net),</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; inputTensorData,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; expectedOutputTensorData,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; backends);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160;}</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;} <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#ae0bf53a96bad08ac5218f3c3747e5bed"> 98</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#ae0bf53a96bad08ac5218f3c3747e5bed">InstanceNormalizationNhwcEndToEndTest1</a>(<span class="keyword">const</span> std::vector&lt;armnn::BackendId&gt;&amp; <a class="code" href="_cl_end_to_end_tests_8cpp.xhtml#ab59caffe2ee6be46c08766c055420f17">defaultBackends</a>)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;{</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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; <span class="keyword">const</span> <span class="keywordtype">float</span> eps = 0.0001f;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> beta = 0.0f;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> gamma = 1.0f;</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="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape{2, 2, 2, 2};</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{2, 2, 2, 2};</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; std::vector&lt;float&gt; inputData = std::vector&lt;float&gt;(</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; 0.f, 1.f,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; 0.f, 2.f,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="comment">// Batch 0, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; 0.f, 2.f,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; 0.f, 4.f,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; <span class="comment">// Batch 1, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; 1.f, -1.f,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; -1.f, 2.f,</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">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; -1.f, -2.f,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; 1.f, 4.f</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; });</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; std::vector&lt;float&gt; expectedOutputData = std::vector&lt;float&gt;(</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; 0.f, -1.1470304f,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; 0.f, -0.22940612f,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="comment">// Batch 0, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; 0.f, -0.22940612f,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; 0.f, 1.6058424f,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; <span class="comment">// Batch 1, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; 0.99995005f, -0.7337929f,</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; -0.99995005f, 0.52413774f,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <span class="comment">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; -0.99995005f, -1.1531031f,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; 0.99995005f, 1.3627582f</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; });</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; InstanceNormalizationEndToEnd(defaultBackends,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>,</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; inputTensorInfo,</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; inputData,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; expectedOutputData,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; gamma,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; beta,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; eps);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160;}</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno"><a class="line" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a120f2896c50cfa77409d16ef6b1628eb"> 168</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a120f2896c50cfa77409d16ef6b1628eb">InstanceNormalizationNchwEndToEndTest1</a>(<span class="keyword">const</span> std::vector&lt;armnn::BackendId&gt;&amp; <a class="code" href="_cl_end_to_end_tests_8cpp.xhtml#ab59caffe2ee6be46c08766c055420f17">defaultBackends</a>)</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; <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> eps = 0.0001f;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> beta = 0.0f;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> gamma = 1.0f;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape{2, 2, 2, 2};</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</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; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{2, 2, 2, 2};</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; std::vector&lt;float&gt; inputData = std::vector&lt;float&gt;(</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; 0.f, 1.f,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; 0.f, 2.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; <span class="comment">// Batch 0, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; 0.f, 2.f,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; 0.f, 4.f,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160;</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; <span class="comment">// Batch 1, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; 1.f, -1.f,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; -1.f, 2.f,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; <span class="comment">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; -1.f, -2.f,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; 1.f, 4.f</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; std::vector&lt;float&gt; expectedOutputData = std::vector&lt;float&gt;(</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"> 207</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; 0.f, -1.1470304f,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; 0.f, -0.22940612f,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; <span class="comment">// Batch 0, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; 0.f, -0.22940612f,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; 0.f, 1.6058424f,</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="comment">// Batch 1, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; 0.99995005f, -0.7337929f,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; -0.99995005f, 0.52413774f,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; <span class="comment">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; -0.99995005f, -1.1531031f,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; 0.99995005f, 1.3627582f</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; });</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; InstanceNormalizationEndToEnd(defaultBackends,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; inputTensorInfo,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; inputData,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; expectedOutputData,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; gamma,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; beta,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; eps);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160;}</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno"><a class="line" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a891c49c919ac2d170b7aa99e23e8871b"> 239</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a891c49c919ac2d170b7aa99e23e8871b">InstanceNormalizationNhwcEndToEndTest2</a>(<span class="keyword">const</span> std::vector&lt;armnn::BackendId&gt;&amp; <a class="code" href="_cl_end_to_end_tests_8cpp.xhtml#ab59caffe2ee6be46c08766c055420f17">defaultBackends</a>)</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; <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> eps = 0.0001f;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> beta = 10.0f;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> gamma = 2.0f;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160;</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape{2, 2, 2, 2};</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{2, 2, 2, 2};</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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"> 253</span>&#160; std::vector&lt;float&gt; inputData = std::vector&lt;float&gt;(</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; 0.f, 1.f,</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; 0.f, 2.f,</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; <span class="comment">// Batch 0, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; 0.f, 2.f,</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; 0.f, 4.f,</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 1, Height 0, Width 0 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; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; -1.f, 2.f,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; <span class="comment">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; -1.f, -2.f,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; 1.f, 4.f</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; });</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; std::vector&lt;float&gt; expectedOutputData = std::vector&lt;float&gt;(</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; {</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; 10.f, 7.7059393f,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; 10.f, 9.541187f,</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 0 x Channel (2)</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; 10.f, 9.541187f,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; 10.f, 13.211685f,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; <span class="comment">// Batch 1, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; 11.9999f, 8.532414f,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; 8.0001f, 11.048275f,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; <span class="comment">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; 8.0001f, 7.693794f,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; 11.9999f, 12.725516f</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; });</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; InstanceNormalizationEndToEnd(defaultBackends,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>,</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; inputTensorInfo,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; inputData,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; expectedOutputData,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; gamma,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; beta,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; eps);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160;}</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160;</div><div class="line"><a name="l00310"></a><span class="lineno"><a class="line" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a3e262db2d488773b8824f73c4f6ab145"> 310</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a3e262db2d488773b8824f73c4f6ab145">InstanceNormalizationNchwEndToEndTest2</a>(<span class="keyword">const</span> std::vector&lt;armnn::BackendId&gt;&amp; <a class="code" href="_cl_end_to_end_tests_8cpp.xhtml#ab59caffe2ee6be46c08766c055420f17">defaultBackends</a>)</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">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> eps = 0.0001f;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> beta = 10.0f;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> gamma = 2.0f;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape{2, 2, 2, 2};</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{2, 2, 2, 2};</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; std::vector&lt;float&gt; inputData = std::vector&lt;float&gt;(</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; {</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; 0.f, 1.f,</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; 0.f, 2.f,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; <span class="comment">// Batch 0, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; 0.f, 2.f,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; 0.f, 4.f,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; <span class="comment">// Batch 1, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; 1.f, -1.f,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; -1.f, 2.f,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; <span class="comment">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; -1.f, -2.f,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; 1.f, 4.f</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; });</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; std::vector&lt;float&gt; expectedOutputData = std::vector&lt;float&gt;(</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"> 349</span>&#160; <span class="comment">// Batch 0, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; 10.f, 7.7059393f,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; <span class="comment">// Batch 0, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; 10.f, 9.541187f,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; <span class="comment">// Batch 0, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; 10.f, 9.541187f,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; <span class="comment">// Batch 0, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160; 10.f, 13.211685f,</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160;</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; <span class="comment">// Batch 1, Height 0, Width 0 x Channel (2)</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; 11.9999f, 8.532414f,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; <span class="comment">// Batch 1, Height 0, Width 1 x Channel (2)</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; 8.0001f, 11.048275f,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; <span class="comment">// Batch 1, Height 1, Width 0 x Channel (2)</span></div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; 8.0001f, 7.693794f,</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; <span class="comment">// Batch 1, Height 1, Width 1 x Channel (2)</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; 11.9999f, 12.725516f</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;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; InstanceNormalizationEndToEnd(defaultBackends,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; inputTensorInfo,</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; outputTensorInfo,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; inputData,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; expectedOutputData,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; gamma,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; beta,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; eps);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160;}</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="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00049">Types.hpp:49</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div>
<div class="ttc" id="_instance_normalization_end_to_end_test_impl_8cpp_xhtml_ae0bf53a96bad08ac5218f3c3747e5bed"><div class="ttname"><a href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#ae0bf53a96bad08ac5218f3c3747e5bed">InstanceNormalizationNhwcEndToEndTest1</a></div><div class="ttdeci">void InstanceNormalizationNhwcEndToEndTest1(const std::vector&lt; armnn::BackendId &gt; &amp;defaultBackends)</div><div class="ttdef"><b>Definition:</b> <a href="_instance_normalization_end_to_end_test_impl_8cpp_source.xhtml#l00098">InstanceNormalizationEndToEndTestImpl.cpp:98</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="_instance_normalization_end_to_end_test_impl_8hpp_xhtml"><div class="ttname"><a href="_instance_normalization_end_to_end_test_impl_8hpp.xhtml">InstanceNormalizationEndToEndTestImpl.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml_a5e078fd505aef7bccaa05c8058e096cc"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">armnn::InstanceNormalizationDescriptor::m_Gamma</a></div><div class="ttdeci">float m_Gamma</div><div class="ttdoc">Gamma, the scale scalar value applied for the normalized tensor. Defaults to 1.0. ...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00647">Descriptors.hpp:647</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="_instance_normalization_end_to_end_test_impl_8cpp_xhtml_a120f2896c50cfa77409d16ef6b1628eb"><div class="ttname"><a href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a120f2896c50cfa77409d16ef6b1628eb">InstanceNormalizationNchwEndToEndTest1</a></div><div class="ttdeci">void InstanceNormalizationNchwEndToEndTest1(const std::vector&lt; armnn::BackendId &gt; &amp;defaultBackends)</div><div class="ttdef"><b>Definition:</b> <a href="_instance_normalization_end_to_end_test_impl_8cpp_source.xhtml#l00168">InstanceNormalizationEndToEndTestImpl.cpp:168</a></div></div>
<div class="ttc" id="_test_utils_8hpp_xhtml"><div class="ttname"><a href="_test_utils_8hpp.xhtml">TestUtils.hpp</a></div></div>
<div class="ttc" id="_end_to_end_test_impl_8hpp_xhtml"><div class="ttname"><a href="_end_to_end_test_impl_8hpp.xhtml">EndToEndTestImpl.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</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="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::InstanceNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Epsilon, small scalar value added to variance to avoid dividing by zero. Defaults to 1e-12f...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00651">Descriptors.hpp:651</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::InstanceNormalizationDescriptor::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#l00653">Descriptors.hpp:653</a></div></div>
<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::InstanceNormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta, the offset scalar value applied for the normalized tensor. Defaults to 1.0. ...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00649">Descriptors.hpp:649</a></div></div>
<div class="ttc" id="_cl_end_to_end_tests_8cpp_xhtml_ab59caffe2ee6be46c08766c055420f17"><div class="ttname"><a href="_cl_end_to_end_tests_8cpp.xhtml#ab59caffe2ee6be46c08766c055420f17">defaultBackends</a></div><div class="ttdeci">std::vector&lt; armnn::BackendId &gt; defaultBackends</div><div class="ttdef"><b>Definition:</b> <a href="_cl_end_to_end_tests_8cpp_source.xhtml#l00025">ClEndToEndTests.cpp:25</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="_instance_normalization_end_to_end_test_impl_8cpp_xhtml_a891c49c919ac2d170b7aa99e23e8871b"><div class="ttname"><a href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a891c49c919ac2d170b7aa99e23e8871b">InstanceNormalizationNhwcEndToEndTest2</a></div><div class="ttdeci">void InstanceNormalizationNhwcEndToEndTest2(const std::vector&lt; armnn::BackendId &gt; &amp;defaultBackends)</div><div class="ttdef"><b>Definition:</b> <a href="_instance_normalization_end_to_end_test_impl_8cpp_source.xhtml#l00239">InstanceNormalizationEndToEndTestImpl.cpp:239</a></div></div>
<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &amp;tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr&lt; INetwork, void(*)(INetwork *network)&gt; INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00101">INetwork.hpp:101</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="structarmnn_1_1_instance_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a></div><div class="ttdoc">An InstanceNormalizationDescriptor for InstanceNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00629">Descriptors.hpp:629</a></div></div>
<div class="ttc" id="_data_layout_utils_8hpp_xhtml"><div class="ttname"><a href="_data_layout_utils_8hpp.xhtml">DataLayoutUtils.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a706f7345af3f18f4b16e226a672214c6"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00049">Network.cpp:49</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="_instance_normalization_end_to_end_test_impl_8cpp_xhtml_a3e262db2d488773b8824f73c4f6ab145"><div class="ttname"><a href="_instance_normalization_end_to_end_test_impl_8cpp.xhtml#a3e262db2d488773b8824f73c4f6ab145">InstanceNormalizationNchwEndToEndTest2</a></div><div class="ttdeci">void InstanceNormalizationNchwEndToEndTest2(const std::vector&lt; armnn::BackendId &gt; &amp;defaultBackends)</div><div class="ttdef"><b>Definition:</b> <a href="_instance_normalization_end_to_end_test_impl_8cpp_source.xhtml#l00310">InstanceNormalizationEndToEndTestImpl.cpp:310</a></div></div>
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