| 87 | <a href="_pooling2d_test_impl_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_pooling2d_test_impl_8hpp.html">Pooling2dTestImpl.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.html">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.html">ResolveType.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_layer_support_8hpp.html">armnn/LayerSupport.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_tensor_utils_8hpp.html">armnnUtils/TensorUtils.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.html">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.html">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="src_2backends_2backends_common_2_workload_info_8hpp.html">backendsCommon/WorkloadInfo.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <<a class="code" href="_tensor_copy_utils_8hpp.html">backendsCommon/test/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <<a class="code" href="_workload_test_utils_8hpp.html">backendsCommon/test/WorkloadTestUtils.hpp</a>></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include <<a class="code" href="_tensor_helpers_8hpp.html">test/TensorHelpers.hpp</a>></span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.html">armnnUtils</a>;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> SimplePooling2dTestImpl(</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  int32_t qOffset,</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& input,</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& outputExpected)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html">armnnUtils::DataLayoutIndexed</a> dimensionIndices = dataLayout;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">auto</span> heightIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">auto</span> widthIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">auto</span> channelsIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[heightIndex]);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[widthIndex]);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[channelsIndex]);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[0]);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[heightIndex]);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[widthIndex]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[channelsIndex]);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = boost::numeric_cast<<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[0]);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  inputBatchSize, inputChannels, inputHeight, inputWidth, dataLayout, ArmnnType);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  outputBatchSize, outputChannels, outputHeight, outputWidth, dataLayout, ArmnnType);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  inputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  }</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> result(outputTensorInfo);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.html">armnn::Pooling2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = descriptor;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="comment">// Don't execute if Pooling is not supported, as an exception will be raised.</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <a class="code" href="classarmnn_1_1_backend_id.html">armnn::BackendId</a> backend = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>();</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> reasonIfUnsupportedMaxLen = 255;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordtype">char</span> reasonIfUnsupported[reasonIfUnsupportedMaxLen+1];</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  result.supported = <a class="code" href="namespacearmnn.html#aea548aa1485adbeeb3e393a13bb6bff8">armnn::IsPooling2dSupported</a>(backend, inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  reasonIfUnsupported, reasonIfUnsupportedMaxLen);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">if</span> (!result.supported)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a6e95afd9a55700cbf6f9e8db8089f2f2">CreatePooling2d</a>(queueDescriptor, workloadInfo);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  inputHandle->Allocate();</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  outputHandle->Allocate();</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  workload->Execute();</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&result.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  result.outputExpected = outputExpected;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> }</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment">//</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment">// Tests max pooling with the following parameters:</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment">//</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment">// Pooling size: 3x3</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment">// Stride: (2,4)</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment">// input size: 8x13</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment">// channels: 2</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="comment">// batch size: 2</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="comment">//</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> SimpleMaxPooling2dSize3x3Stride2x4TestCommon(</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordtype">bool</span> forceNoPadding,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 4;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// forceNoPadding is mainly used for compatibility with ARM Compute.</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size.</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = forceNoPadding ? 0 : 3;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.html#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 8;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 13;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth =</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  (inputWidth + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>) /</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight =</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  (inputHeight + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>) /</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 2;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  std::vector<float> singleChannelData({</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  });</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// Constructs input data.</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  std::vector<float> inputData;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">auto</span> negator = [](<span class="keywordtype">float</span> f) { <span class="keywordflow">return</span> -f; };</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// First image (two channels where the second channel is the negative of the first one).</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end());</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="comment">// Second image (same as first image).</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end());</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputData, qScale, qOffset));</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="comment">// These were calculated manually.</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keyword">auto</span> shape(GetTensorShapeAsArray<4>(outputTensorInfo));</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  boost::multi_array<T, 4> outputExpected(shape);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordflow">if</span> (forceNoPadding)</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  {</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  QuantizedVector<T>({</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  8.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  9.0f, 7.0f, 9.0f,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  9.0f, 9.0f, 9.0f,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  0.0f, 0.0f, -3.0f,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  -1.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  -1.0f, -1.0f, -1.0f,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  8.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  9.0f, 7.0f, 9.0f,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  9.0f, 9.0f, 9.0f,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  0.0f, 0.0f, -3.0f,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  -1.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  -1.0f, -1.0f, -1.0f</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  },</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  qScale, qOffset));</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  QuantizedVector<T>({</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f,</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  0.0f, 0.0f, 0.0f, 0.0f,-3.0f,-3.0f,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  0.0f,-1.0f, 0.0f, 0.0f, 0.0f,-2.0f,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  0.0f,-1.0f,-1.0f,-1.0f,-1.0f,-1.0f,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  0.0f, 0.0f, 0.0f, 0.0f,-3.0f,-3.0f,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  0.0f,-1.0f, 0.0f, 0.0f, 0.0f,-2.0f,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  0.0f,-1.0f,-1.0f,-1.0f,-1.0f,-1.0f</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  },</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  qScale, qOffset));</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  }</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> }</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> SimpleMaxPooling2dTestCommon(</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 2, 4, 4, dataLayout, ArmnnType);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 2, 2, 2, dataLayout, ArmnnType);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  {</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  }</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  std::vector<T> inputData(</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  QuantizedVector<T>({</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  1.0f, 2.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  3.0f, 4.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  9.0f, 10.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  11.0f, 12.0f, 15.0f, 16.0f,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  17.0f, 18.0f, 21.0f, 22.0f,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  19.0f, 20.0f, 23.0f, 24.0f,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  25.0f, 26.0f, 29.0f, 30.0f,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  27.0f, 28.0f, 31.0f, 32.0f,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  },</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  qScale, qOffset));</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  std::vector<T> outputData(</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  QuantizedVector<T>({</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  4.0f, 8.0f,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  12.0f, 16.0f,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  20.0f, 24.0f,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  28.0f, 32.0f,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  },</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  qScale, qOffset));</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  {</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  inputData = tmp;</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::vector<T> tmp1(outputData.size());</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp1.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  outputData = tmp1;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  }</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> </div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> SimpleAveragePooling2dTestCommon(</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> {</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 2, 4, 4, dataLayout, ArmnnType);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 2, 2, 2, dataLayout, ArmnnType);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  std::vector<T> inputData(</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  QuantizedVector<T>({</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  2.0f, 2.0f, 6.0f, 6.0f,</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  4.0f, 4.0f, 8.0f, 8.0f,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  10.0f, 12.0f, 14.0f, 16.0f,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  10.0f, 12.0f, 16.0f, 14.0f,</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> </div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  18.0f, 20.0f, 24.0f, 22.0f,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  20.0f, 18.0f, 22.0f, 24.0f,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  26.0f, 28.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  },</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  qScale, qOffset));</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  std::vector<T> outputData(</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  QuantizedVector<T>({</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  3.0f, 7.0f,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  11.0f, 15.0f,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  19.0f, 23.0f,</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  27.0f, 0.0f,</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  },</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  qScale, qOffset));</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  {</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  inputData = tmp;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  std::vector<T> tmp1(outputData.size());</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp1.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  outputData = tmp1;</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  }</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> }</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> LargeTensorsAveragePooling2dTestCommon(</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> {</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 100;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 5;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 50;</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 50;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 50;</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 50;</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> </div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 5, 3, 52, 60 }, ArmnnType);</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 5, 3, 11, 13 }, ArmnnType);</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  {</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  }</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  std::vector<T> inputVec;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(); ++i)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  inputVec.push_back(1);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  }</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, inputVec);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  std::vector<T> outputVec;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(); ++i)</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  outputVec.push_back(1);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  }</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputVec);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> }</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> SimpleL2Pooling2dTestCommon(</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> {</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 2, 4, 4, dataLayout, ArmnnType);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.html#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 2, 2, 2, dataLayout, ArmnnType);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  std::vector<T> inputData(</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  QuantizedVector<T>({</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  1.0f, 7.0f, 5.0f, 5.0f,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  1.0f, 7.0f, 5.0f, 5.0f,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  3.0f, 3.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  3.0f, 3.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  1.0f, 7.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  1.0f, 7.0f, 2.0f, 0.0f,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  0.0f, 2.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  0.0f, 0.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  },</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  qScale, qOffset));</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  std::vector<T> outputData(</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  QuantizedVector<T>({</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  5.0f, 5.0f,</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  3.0f, 1.0f,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  5.0f, 1.0f,</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  1.0f, 1.0f,</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  },</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  qScale, qOffset));</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span> </div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  {</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  inputData = tmp;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> </div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  std::vector<T> tmp1(outputData.size());</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp1.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  outputData = tmp1;</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  }</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span> </div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> }</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> L2Pooling2dSize3Stride1TestCommon(</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> {</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  QuantizedVector<T>({</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  2.0f, 1.0f, 5.0f, 2.0f,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  1.0f, 2.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  5.0f, 4.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  2.0f, 1.0f, 5.0f, 2.0f,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  },</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  qScale, qOffset));</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 2, 2 }, ArmnnType);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  QuantizedVector<T>({</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  3.0f, 3.0f,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  3.0f, 3.0f,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  },</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  qScale, qOffset));</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> }</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> L2Pooling2dSize3Stride3TestCommon(</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> {</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 3;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> </div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 9, 9 }, ArmnnType);</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  QuantizedVector<T>({</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  },</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  qScale, qOffset));</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3 }, ArmnnType);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  QuantizedVector<T>({</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  3.0f, 3.0f, 3.0f,</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  3.0f, 3.0f, 3.0f,</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  3.0f, 3.0f, 3.0f,</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  },</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  qScale, qOffset));</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> }</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> </div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> L2Pooling2dSize3Stride4TestCommon(</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> {</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 4;</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> </div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 7, 7 }, ArmnnType);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  QuantizedVector<T>({</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  },</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  qScale, qOffset));</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 2, 2 }, ArmnnType);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  QuantizedVector<T>({</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  3.0f, 3.0f,</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  3.0f, 3.0f,</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  },</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  qScale, qOffset));</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> </div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> }</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> L2Pooling2dSize7TestCommon(</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> {</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 7;</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 7;</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span> </div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 7, 7 }, ArmnnType);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  QuantizedVector<T>({</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f,</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f,</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f,</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  },</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  qScale, qOffset));</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span> </div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 1, 1 }, ArmnnType);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  QuantizedVector<T>({</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  3.0f,</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  },</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  qScale, qOffset));</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span> </div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> }</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> </div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> L2Pooling2dSize9TestCommon(</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span> {</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 9;</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 9;</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span> </div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 9, 9 }, ArmnnType);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  QuantizedVector<T>({</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  },</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  qScale, qOffset));</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 1, 1 }, ArmnnType);</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  QuantizedVector<T>({</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  3.0f,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  },</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  qScale, qOffset));</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> </div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span> }</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> </div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> AsymmetricNonSquarePooling2dTestCommon(</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> {</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 1, 3 }, ArmnnType);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 2, 2 }, ArmnnType);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> </div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  descriptor.m_PoolWidth = 2;</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  descriptor.m_PoolHeight = 3;</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  descriptor.m_StrideX = 2;</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  descriptor.m_StrideY = 1;</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  descriptor.m_PadLeft = 2;</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  descriptor.m_PadRight = 0;</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  descriptor.m_PadTop = 1;</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  descriptor.m_PadBottom = 2;</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  descriptor.m_OutputShapeRounding = <a class="code" href="namespacearmnn.html#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  descriptor.m_PaddingMethod = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> </div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <span class="comment">// Construct input data.</span></div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  QuantizedVector<T>({</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  },</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  qScale, qOffset));</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> </div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="comment">// These were calculated manually.</span></div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  QuantizedVector<T>({</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  0.0f, 3.0f, 0.0f, 3.0f,</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  },</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  qScale, qOffset));</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span> </div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> }</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span> </div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> ComparePooling2dTestCommon(</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> {</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 32;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelCount = 2;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 5;</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span> </div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> poolSize = 3;</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = 2;</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = 4;</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padX = 0;</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padY = 0;</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span> </div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX;</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY;</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> </div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { batchSize, channelCount, inputHeight, inputWidth };</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { batchSize, channelCount, outputHeight, outputWidth };</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> </div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, inputShape, ArmnnType);</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, outputShape, ArmnnType);</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> </div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  {</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  }</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span> </div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 81715);</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span> </div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> comparisonResult(outputTensorInfo);</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span> </div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> </div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.html">armnn::Pooling2dQueueDescriptor</a> data;</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = poolingType;</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = poolSize;</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = poolSize;</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padX;</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padX;</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padY;</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padY;</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.html#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span> </div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span> </div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <span class="comment">// Don't execute if Pooling is not supported, as an exception will be raised.</span></div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <a class="code" href="classarmnn_1_1_backend_id.html">armnn::BackendId</a> backend = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>();</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> reasonIfUnsupportedMaxLen = 255;</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <span class="keywordtype">char</span> reasonIfUnsupported[reasonIfUnsupportedMaxLen+1];</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  comparisonResult.supported = <a class="code" href="namespacearmnn.html#aea548aa1485adbeeb3e393a13bb6bff8">armnn::IsPooling2dSupported</a>(backend, inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  reasonIfUnsupported, reasonIfUnsupportedMaxLen);</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="keywordflow">if</span> (!comparisonResult.supported)</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  {</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <span class="keywordflow">return</span> comparisonResult;</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  }</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> </div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.html">armnn::Pooling2dQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> </div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a6e95afd9a55700cbf6f9e8db8089f2f2">CreatePooling2d</a>(data, info);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a6e95afd9a55700cbf6f9e8db8089f2f2">CreatePooling2d</a>(refData, refInfo);</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span> </div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  outputHandleRef->Allocate();</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  inputHandleRef->Allocate();</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  inputHandle->Allocate();</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  outputHandle->Allocate();</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span> </div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  workload->Execute();</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  workloadRef->Execute();</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&comparisonResult.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get());</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span> </div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <span class="keywordflow">return</span> comparisonResult;</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span> }</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> </div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span> <span class="comment">//</span></div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span> <span class="comment">// Tests max pooling with the following parameters:</span></div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span> <span class="comment">//</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> <span class="comment">// Pooling size: 2x2</span></div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span> <span class="comment">// Stride: (2,2)</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span> <span class="comment">// input size: 4x4</span></div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> <span class="comment">// channels: 1</span></div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span> <span class="comment">// batch size: 1</span></div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span> <span class="comment">//</span></div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> SimpleMaxPooling2dSize2x2Stride2x2TestCommon(</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <span class="keywordtype">bool</span> forceNoPadding,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span> {</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = forceNoPadding ? 0 : 3;</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.html#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span> </div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span> </div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 4;</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span> </div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 4;</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth =</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  (inputWidth + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>) /</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight =</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  (inputHeight + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>) /</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 1;</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 1;</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span> </div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  510.0f, 222.0f, 780.0f, 654.0f,</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  141.0f, 276.0f, 15.0f, 546.0f,</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  303.0f, 618.0f, 582.0f, 339.0f,</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  438.0f, 564.0f, 573.0f, 402.0f</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  };</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> </div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  <span class="comment">// Note that left and right edges will be 0.f, due to the 2x2 max pooling only accessing zeros here.</span></div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  std::vector<float> expectedOutputDataWithPadding = {</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  0.0f, 510.0f, 780.0f, 654.0f, 0.0f,</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  0.0f, 438.0f, 618.0f, 402.0f, 0.0f</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  };</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> </div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  std::vector<float> expectedOutputDataNoPadding = {</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  510.0f, 780.0f,</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  618.0f, 582.0f</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  };</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> </div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> </div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="comment">// Scale and offset should match input - we're just calculating maximum values.</span></div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span> </div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  {</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  }</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> </div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputData, qScale, qOffset));</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> </div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  forceNoPadding ? QuantizedVector<T>(expectedOutputDataNoPadding, qScale, qOffset) :</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  QuantizedVector<T>(expectedOutputDataWithPadding, qScale, qOffset));</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span> </div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span> }</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span> </div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> <span class="comment">//</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span> <span class="comment">// Tests max pooling with the following parameters:</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span> <span class="comment">//</span></div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> <span class="comment">// Pooling size: 3x2</span></div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span> <span class="comment">// Stride: (2,2)</span></div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span> <span class="comment">// input size: 3x2</span></div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span> <span class="comment">// channels: 1</span></div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> <span class="comment">// batch size: 1</span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span> <span class="comment">//</span></div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon(</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="keywordtype">bool</span> forceNoPadding,</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span> {</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = (forceNoPadding) ? 0 : 1;</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>;</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.html#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span> </div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3;</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2;</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth =</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  (inputWidth + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>) /</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight =</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  (inputHeight + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>) /</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 1;</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 1;</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span> </div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  3.0f, 6.0f, 9.0f,</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  12.0f, 15.0f, 18.0f,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  };</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span> </div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  std::vector<float> expectedOutputDataWithPadding = {</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  6.0f, 8.0f,</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  };</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span> </div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  std::vector<float> expectedOutputDataNoPadding = {</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  10.5f,</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  };</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span> </div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span> </div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="comment">// Scale and offset should match input - we're just calculating average values.</span></div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span> </div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  {</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  }</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> </div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputData, qScale, qOffset));</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span> </div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  forceNoPadding ? QuantizedVector<T>(expectedOutputDataNoPadding, qScale, qOffset) :</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  QuantizedVector<T>(expectedOutputDataWithPadding, qScale, qOffset));</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span> </div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span> }</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span> </div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span> </div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingSimpleMaxPooling2dTestCommon(</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  int32_t qOffset = 0)</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> {</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span> </div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3 }, ArmnnType);</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span> </div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  {</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  }</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span> </div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  QuantizedVector<T>({</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  },</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  qScale, qOffset));</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> </div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  QuantizedVector<T>({</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  -1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  1.0f, 2.0f, -4.0f,</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  },</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  qScale, qOffset));</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> </div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> }</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> </div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingMaxPooling2dSize3TestCommon(</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  int32_t qOffset = 0)</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> {</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> </div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> </div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  {</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  }</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> </div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  QuantizedVector<T>({</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  },</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  qScale, qOffset));</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span> </div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  QuantizedVector<T>({</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  -1.0f, 3.0f, 4.0f, 4.0f,</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  2.0f, 3.0f, 4.0f, 4.0f,</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  2.0f, 3.0f, 4.0f, 4.0f,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  2.0f, 2.0f, 2.0f, -3.0f,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  },</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  qScale, qOffset));</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> </div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span> }</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> </div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingSimpleAveragePooling2dTestCommon(</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  int32_t qOffset = 0)</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> {</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> </div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3 }, ArmnnType);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> </div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  {</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  }</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> </div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  QuantizedVector<T>({</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  },</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  qScale, qOffset));</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> </div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  QuantizedVector<T>({</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  3.0f, 13.0f, 10.0f,</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  6.0f, 26.0f, 20.0f,</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  3.0f, 13.0f, 10.0f,</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  },</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  qScale, qOffset));</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span> </div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span> }</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span> </div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  int32_t qOffset = 0)</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span> {</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.html#adf2e5515c4c36a3e7e46bb8b83c6754ea3237fbc8204064c106cb9080088a17cb">armnn::OutputShapeRounding::Ceiling</a>;</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span> </div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 2, 2 }, ArmnnType);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> </div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  {</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  }</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span> </div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  QuantizedVector<T>({</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  },</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  qScale, qOffset));</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> </div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  QuantizedVector<T>({</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  2.0f, 3.5f,</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  2.0f, 3.5f</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  },</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  qScale, qOffset));</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span> }</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> </div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingAveragePooling2dSize3TestCommon(</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  int32_t qOffset = 0)</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span> {</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span> </div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> </div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  {</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  }</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span> </div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  QuantizedVector<T>({</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  9.0f, 27.0f, 18.0f, 36.0f,</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  18.0f, 9.0f, 18.0f, 9.0f,</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  27.0f, 18.0f, 9.0f, 27.0f,</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  9.0f, 27.0f, 9.0f, 18.0f,</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  },</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  qScale, qOffset));</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> </div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  QuantizedVector<T>({</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  7.0f, 11.0f, 13.0f, 9.0f,</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  12.0f, 17.0f, 19.0f, 13.0f,</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  12.0f, 16.0f, 16.0f, 10.0f,</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  9.0f, 11.0f, 12.0f, 7.0f,</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  },</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  qScale, qOffset));</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span> </div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span> }</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span> </div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingSimpleL2Pooling2dTestCommon(</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  int32_t qOffset = 0)</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span> {</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span> </div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3 }, ArmnnType);</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span> </div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  {</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  }</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span> </div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  QuantizedVector<T>({</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  2.0f, 4.0f, 8.0f, 16.0f,</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  4.0f, 2.0f, 2.0f, 4.0f,</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  8.0f, 2.0f, 4.0f, 2.0f,</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  16.0f, 2.0f, 2.0f, 8.0f,</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  },</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  qScale, qOffset));</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span> </div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  QuantizedVector<T>({</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  1.0f, 4.4721f, 8.0f,</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  4.4721f, 2.6457f, 2.236f,</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  8.0f, 1.4142f, 4.0f,</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  },</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  qScale, qOffset));</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span> </div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span> }</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span> </div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> IgnorePaddingL2Pooling2dSize3TestCommon(</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  int32_t qOffset = 0)</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span> {</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span> </div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span> </div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  {</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  }</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span> </div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  QuantizedVector<T>({</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  },</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  qScale, qOffset));</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span> </div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  <span class="keyword">auto</span> outputExpected = MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  QuantizedVector<T>({</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  1.0540f, 1.7638f, 2.5385f, 2.3570f,</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  1.2909f, 2.1602f, 3.1091f, 2.8867f,</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  1.2909f, 2.1602f, 3.1091f, 2.8867f,</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  1.0540f, 1.7638f, 2.5385f, 2.3570f,</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  },</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  qScale, qOffset));</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span> </div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <span class="keywordflow">return</span> SimplePooling2dTestImpl<ArmnnType>(</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected);</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span> }</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span> </div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span> </div><div class="line"><a name="l01355"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a0f8bd9f2e91b9b2aad21e2728bb655e3"> 1355</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a0f8bd9f2e91b9b2aad21e2728bb655e3">SimpleMaxPooling2dSize2x2Stride2x2Test</a>(</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  <span class="keywordtype">bool</span> forceNoPadding)</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span> {</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dSize2x2Stride2x2TestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  workloadFactory, memoryManager, forceNoPadding);</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span> }</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span> </div><div class="line"><a name="l01364"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a6f4185540ddce123892c799e516ee50d"> 1364</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a6f4185540ddce123892c799e516ee50d">SimpleMaxPooling2dSize2x2Stride2x2Uint8Test</a>(</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  <span class="keywordtype">bool</span> forceNoPadding)</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span> {</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dSize2x2Stride2x2TestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  workloadFactory, memoryManager, forceNoPadding, 3.0f, -5);</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> }</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span> </div><div class="line"><a name="l01373"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a50dff405960b48e03ee0d296f72743df"> 1373</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a50dff405960b48e03ee0d296f72743df">SimpleMaxPooling2dSize2x2Stride2x2Int16Test</a>(</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <span class="keywordtype">bool</span> forceNoPadding)</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> {</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dSize2x2Stride2x2TestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  workloadFactory, memoryManager, forceNoPadding);</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span> }</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span> </div><div class="line"><a name="l01382"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a2f7ec646738a0e279cfbb77afb3e41bd"> 1382</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a2f7ec646738a0e279cfbb77afb3e41bd">SimpleMaxPooling2dSize3x3Stride2x4Test</a>(</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <span class="keywordtype">bool</span> forceNoPadding)</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span> {</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dSize3x3Stride2x4TestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  workloadFactory, memoryManager, forceNoPadding);</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span> }</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span> </div><div class="line"><a name="l01391"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#aacd91233b18641d11b190969bcd93057"> 1391</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#aacd91233b18641d11b190969bcd93057">SimpleMaxPooling2dSize3x3Stride2x4Uint8Test</a>(</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <span class="keywordtype">bool</span> forceNoPadding)</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span> {</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dSize3x3Stride2x4TestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  workloadFactory, memoryManager, forceNoPadding, 0.1f, 128);</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span> }</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span> </div><div class="line"><a name="l01400"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ae398f1e979dd0ad467a8f5182b9101ee"> 1400</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ae398f1e979dd0ad467a8f5182b9101ee">SimpleMaxPooling2dSize3x3Stride2x4Int16Test</a>(</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  <span class="keywordtype">bool</span> forceNoPadding)</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span> {</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dSize3x3Stride2x4TestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  workloadFactory, memoryManager, forceNoPadding);</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span> }</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span> </div><div class="line"><a name="l01409"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a2783cdc0a074cbdfbf2f91e116c92c97"> 1409</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a2783cdc0a074cbdfbf2f91e116c92c97">SimpleMaxPooling2dTest</a>(</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span> {</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span> }</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> </div><div class="line"><a name="l01417"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a807ea3c4451f81f5b91b7db53eb0a138"> 1417</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a807ea3c4451f81f5b91b7db53eb0a138">SimpleMaxPooling2dUint8Test</a>(</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span> {</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span> }</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span> </div><div class="line"><a name="l01425"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a5ff218665f1e7dc5b90c395027573e8c"> 1425</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a5ff218665f1e7dc5b90c395027573e8c">SimpleMaxPooling2dInt16Test</a>(</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span> {</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  <span class="keywordflow">return</span> SimpleMaxPooling2dTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span> }</div><div class="line"><a name="l01432"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a2008735411bf96a7febef693c41a4ff5"> 1432</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a2008735411bf96a7febef693c41a4ff5">IgnorePaddingSimpleMaxPooling2dTest</a>(</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span> {</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleMaxPooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span> }</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span> </div><div class="line"><a name="l01439"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a3789eb1689edeed1aae83c773e75607c"> 1439</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a3789eb1689edeed1aae83c773e75607c">IgnorePaddingSimpleMaxPooling2dUint8Test</a>(</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span> {</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleMaxPooling2dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  workloadFactory, memoryManager, 1.0f, -5);</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span> }</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span> </div><div class="line"><a name="l01447"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#af0a9b7e26de79a55506a3cd3d36a83a7"> 1447</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#af0a9b7e26de79a55506a3cd3d36a83a7">IgnorePaddingSimpleMaxPooling2dInt16Test</a>(</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span> {</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleMaxPooling2dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span> }</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> </div><div class="line"><a name="l01455"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a08f2f1d9a1f69a5799294a881dbb24b4"> 1455</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a08f2f1d9a1f69a5799294a881dbb24b4">IgnorePaddingMaxPooling2dSize3Test</a>(</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span> {</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <span class="keywordflow">return</span> IgnorePaddingMaxPooling2dSize3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span> }</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span> </div><div class="line"><a name="l01462"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a7f7147713ac3346b30c1071bf14fb374"> 1462</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a7f7147713ac3346b30c1071bf14fb374">IgnorePaddingMaxPooling2dSize3Uint8Test</a>(</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span> {</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  <span class="keywordflow">return</span> IgnorePaddingMaxPooling2dSize3TestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  workloadFactory, memoryManager, 1.0f, -5);</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span> }</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span> </div><div class="line"><a name="l01470"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#acf9c19888a6f2139b355052d542920bb"> 1470</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#acf9c19888a6f2139b355052d542920bb">IgnorePaddingMaxPooling2dSize3Int16Test</a>(</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span> {</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  <span class="keywordflow">return</span> IgnorePaddingMaxPooling2dSize3TestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span> }</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span> </div><div class="line"><a name="l01478"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a12f407a57b0a6ae541ad67275e398788"> 1478</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a12f407a57b0a6ae541ad67275e398788">SimpleAveragePooling2dTest</a>(</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span> {</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  <span class="keywordflow">return</span> SimpleAveragePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> }</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span> </div><div class="line"><a name="l01486"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a1b114f8624e335814f7a17856669ada2"> 1486</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a1b114f8624e335814f7a17856669ada2">SimpleAveragePooling2dUint8Test</a>(</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span> {</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  <span class="keywordflow">return</span> SimpleAveragePooling2dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  workloadFactory, memoryManager, dataLayout, 0.5, -1);</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span> }</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span> </div><div class="line"><a name="l01495"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a0040a2bec5090be39bc6c4382fb7b6ee"> 1495</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a0040a2bec5090be39bc6c4382fb7b6ee">SimpleAveragePooling2dInt16Test</a>(</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span> {</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  <span class="keywordflow">return</span> SimpleAveragePooling2dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span> }</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span> </div><div class="line"><a name="l01504"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#adb17ca1fb168506bdd494149525c4dea"> 1504</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#adb17ca1fb168506bdd494149525c4dea">IgnorePaddingAveragePooling2dSize3x2Stride2x2Test</a>(</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <span class="keywordtype">bool</span> forceNoPadding)</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span> {</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <span class="keywordflow">return</span> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  workloadFactory, memoryManager, forceNoPadding);</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span> }</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span> </div><div class="line"><a name="l01513"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a3929c1959366adb6236ad41acee93b19"> 1513</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a3929c1959366adb6236ad41acee93b19">LargeTensorsAveragePooling2dTest</a>(</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span> {</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <span class="keywordflow">return</span> LargeTensorsAveragePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span> }</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span> </div><div class="line"><a name="l01520"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a6b0562007adce4063f111fa1e90e4344"> 1520</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a6b0562007adce4063f111fa1e90e4344">LargeTensorsAveragePooling2dUint8Test</a>(</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span> {</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  <span class="keywordflow">return</span> LargeTensorsAveragePooling2dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  workloadFactory, memoryManager, 0.5, -1);</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span> }</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> </div><div class="line"><a name="l01528"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#afb670e621e8c15f457eb0b178ff70f93"> 1528</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#afb670e621e8c15f457eb0b178ff70f93">LargeTensorsAveragePooling2dInt16Test</a>(</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span> {</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  <span class="keywordflow">return</span> LargeTensorsAveragePooling2dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span> }</div><div class="line"><a name="l01535"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a5103df4c034f9679776cd55e81cd93a4"> 1535</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a5103df4c034f9679776cd55e81cd93a4">IgnorePaddingSimpleAveragePooling2dTest</a>(</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span> {</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span> }</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> </div><div class="line"><a name="l01542"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#afdc8b9898475e00425b125447eb0bf3e"> 1542</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#afdc8b9898475e00425b125447eb0bf3e">IgnorePaddingSimpleAveragePooling2dUint8Test</a>(</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span> {</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling2dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span> }</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span> </div><div class="line"><a name="l01550"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a13ccef523e801fb5fdc2868fae871a26"> 1550</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a13ccef523e801fb5fdc2868fae871a26">IgnorePaddingSimpleAveragePooling2dInt16Test</a>(</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span> {</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling2dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span> }</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span> </div><div class="line"><a name="l01558"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a19f29e6ac7af2f7ee8316048c6638aff"> 1558</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a19f29e6ac7af2f7ee8316048c6638aff">IgnorePaddingSimpleAveragePooling2dNoPaddingTest</a>(</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span> {</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span> }</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span> </div><div class="line"><a name="l01566"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#abdb3d542a8c5a5e6a42cb91e3ebce21f"> 1566</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#abdb3d542a8c5a5e6a42cb91e3ebce21f">IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test</a>(</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span> {</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> }</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span> </div><div class="line"><a name="l01574"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a7c0c120c3d2c63941fd2dec93b7d9564"> 1574</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a7c0c120c3d2c63941fd2dec93b7d9564">IgnorePaddingSimpleAveragePooling2dNoPaddingInt16Test</a>(</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span> {</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span> }</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span> </div><div class="line"><a name="l01582"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a01264032fbe8272556bf1142b7cd74b1"> 1582</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a01264032fbe8272556bf1142b7cd74b1">IgnorePaddingAveragePooling2dSize3Test</a>(</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span> {</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  <span class="keywordflow">return</span> IgnorePaddingAveragePooling2dSize3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span> }</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span> </div><div class="line"><a name="l01589"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ad5690176a9dd35986a5e895f1378efc0"> 1589</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ad5690176a9dd35986a5e895f1378efc0">IgnorePaddingAveragePooling2dSize3Uint8Test</a>(</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span> {</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  <span class="keywordflow">return</span> IgnorePaddingAveragePooling2dSize3TestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> }</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span> </div><div class="line"><a name="l01597"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a3a693fe529564ec9bdf6b66965b0083e"> 1597</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a3a693fe529564ec9bdf6b66965b0083e">IgnorePaddingAveragePooling2dSize3Int16Test</a>(</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span> {</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  <span class="keywordflow">return</span> IgnorePaddingAveragePooling2dSize3TestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  workloadFactory, memoryManager);</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span> }</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span> </div><div class="line"><a name="l01605"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a26dc25f8fe0401dd5b9c1c733ed14f3d"> 1605</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a26dc25f8fe0401dd5b9c1c733ed14f3d">SimpleL2Pooling2dTest</a>(</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span> {</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  <span class="keywordflow">return</span> SimpleL2Pooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span> }</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span> </div><div class="line"><a name="l01613"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#aafecf98426773306be1715559ea4019e"> 1613</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#aafecf98426773306be1715559ea4019e">SimpleL2Pooling2dUint8Test</a>(</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span> {</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  <span class="keywordflow">return</span> SimpleL2Pooling2dTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span> }</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span> </div><div class="line"><a name="l01621"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a9ed42b523afa1b8017f75478bf90d28b"> 1621</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a9ed42b523afa1b8017f75478bf90d28b">SimpleL2Pooling2dInt16Test</a>(</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span> {</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  <span class="keywordflow">return</span> SimpleL2Pooling2dTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, dataLayout);</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span> }</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span> </div><div class="line"><a name="l01629"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a8d68b7bc57ed5234008b9cc8f67f13ae"> 1629</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a8d68b7bc57ed5234008b9cc8f67f13ae">L2Pooling2dSize3Stride1Test</a>(</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span> {</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride1TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span> }</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span> </div><div class="line"><a name="l01636"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#adfc1ba9f35e1c8657ba32d3d6d56a76e"> 1636</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#adfc1ba9f35e1c8657ba32d3d6d56a76e">L2Pooling2dSize3Stride1Uint8Test</a>(</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span> {</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride1TestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> }</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span> </div><div class="line"><a name="l01643"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a35c905df849b9042cf2b1d64b673018e"> 1643</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a35c905df849b9042cf2b1d64b673018e">L2Pooling2dSize3Stride1Int16Test</a>(</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> {</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride1TestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span> }</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span> </div><div class="line"><a name="l01650"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ae82ddbd442401119c0d873cc08384ba4"> 1650</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ae82ddbd442401119c0d873cc08384ba4">L2Pooling2dSize3Stride3Test</a>(</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span> {</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span> }</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span> </div><div class="line"><a name="l01657"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a8c53d690773392aeeaa0eeae95fd16e2"> 1657</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a8c53d690773392aeeaa0eeae95fd16e2">L2Pooling2dSize3Stride3Uint8Test</a>(</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span> {</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride3TestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> }</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span> </div><div class="line"><a name="l01664"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a89809041249c49e29272cabb382e6898"> 1664</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a89809041249c49e29272cabb382e6898">L2Pooling2dSize3Stride3Int16Test</a>(</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span> {</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride3TestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span> }</div><div class="line"><a name="l01670"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#aa9dce9d99b3c10eedf8abfd853478e0a"> 1670</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#aa9dce9d99b3c10eedf8abfd853478e0a">L2Pooling2dSize3Stride4Test</a>(</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span> {</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride4TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span> }</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span> </div><div class="line"><a name="l01677"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#af936b77fe82b71e5cbd58cad48b1bfc2"> 1677</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#af936b77fe82b71e5cbd58cad48b1bfc2">L2Pooling2dSize3Stride4Uint8Test</a>(</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span> {</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride4TestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span> }</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span> </div><div class="line"><a name="l01684"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#afbe0dfcc631615f3de96b415788e5630"> 1684</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#afbe0dfcc631615f3de96b415788e5630">L2Pooling2dSize3Stride4Int16Test</a>(</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span> {</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  <span class="keywordflow">return</span> L2Pooling2dSize3Stride4TestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span> }</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span> </div><div class="line"><a name="l01691"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ae4591d1175ba7115661b8eb80745cb64"> 1691</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ae4591d1175ba7115661b8eb80745cb64">L2Pooling2dSize7Test</a>(</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span> {</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  <span class="keywordflow">return</span> L2Pooling2dSize7TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span> }</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span> </div><div class="line"><a name="l01698"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a7a85e2ce7c2117c9e2ab829be378deb0"> 1698</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a7a85e2ce7c2117c9e2ab829be378deb0">L2Pooling2dSize7Uint8Test</a>(</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span> {</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  <span class="keywordflow">return</span> L2Pooling2dSize7TestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span> }</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span> </div><div class="line"><a name="l01705"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a819c382960e69594f22f5e11a9fbf5bb"> 1705</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a819c382960e69594f22f5e11a9fbf5bb">L2Pooling2dSize7Int16Test</a>(</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span> {</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  <span class="keywordflow">return</span> L2Pooling2dSize7TestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span> }</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span> </div><div class="line"><a name="l01712"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ae6eec78e8c9af37214d683eb97085ffb"> 1712</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ae6eec78e8c9af37214d683eb97085ffb">L2Pooling2dSize9Test</a>(</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span> {</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <span class="keywordflow">return</span> L2Pooling2dSize9TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span> }</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span> </div><div class="line"><a name="l01719"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a416503aafd0e95894ff1d40bf4b9750e"> 1719</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a416503aafd0e95894ff1d40bf4b9750e">L2Pooling2dSize9Uint8Test</a>(</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span> {</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  <span class="keywordflow">return</span> L2Pooling2dSize9TestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span> }</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span> </div><div class="line"><a name="l01726"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a23dfab8d454bf41fccb664a0cfce3db2"> 1726</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a23dfab8d454bf41fccb664a0cfce3db2">L2Pooling2dSize9Int16Test</a>(</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span> {</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <span class="keywordflow">return</span> L2Pooling2dSize9TestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span> }</div><div class="line"><a name="l01732"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#aa89af1e396c0f689aa6078f6a3f45825"> 1732</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#aa89af1e396c0f689aa6078f6a3f45825">IgnorePaddingSimpleL2Pooling2dTest</a>(</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span> {</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleL2Pooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span> }</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span> </div><div class="line"><a name="l01739"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#aab64d2d563a7dbca5e5f47d95774ac52"> 1739</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#aab64d2d563a7dbca5e5f47d95774ac52">IgnorePaddingSimpleL2Pooling2dUint8Test</a>(</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span> {</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleL2Pooling2dTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span> }</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span> </div><div class="line"><a name="l01746"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a9b1409ed5591fd540c6102628897ebf6"> 1746</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a9b1409ed5591fd540c6102628897ebf6">IgnorePaddingSimpleL2Pooling2dInt16Test</a>(</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span> {</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleL2Pooling2dTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span> }</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span> </div><div class="line"><a name="l01753"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ac5c1d6307ea085e55299611717f17756"> 1753</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ac5c1d6307ea085e55299611717f17756">IgnorePaddingL2Pooling2dSize3Test</a>(</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span> {</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  <span class="keywordflow">return</span> IgnorePaddingL2Pooling2dSize3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span> }</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span> </div><div class="line"><a name="l01760"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a671584b349d7c94cd7c108c8507ba149"> 1760</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a671584b349d7c94cd7c108c8507ba149">IgnorePaddingL2Pooling2dSize3Uint8Test</a>(</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span> {</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  <span class="keywordflow">return</span> IgnorePaddingL2Pooling2dSize3TestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span> }</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span> </div><div class="line"><a name="l01767"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ab721b365fc476b3917abe60c802823b7"> 1767</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ab721b365fc476b3917abe60c802823b7">IgnorePaddingL2Pooling2dSize3Int16Test</a>(</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span> {</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  <span class="keywordflow">return</span> IgnorePaddingL2Pooling2dSize3TestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span> }</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span> </div><div class="line"><a name="l01774"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a2b1ede7e8d8e5dad79d99030f57b8745"> 1774</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a2b1ede7e8d8e5dad79d99030f57b8745">AsymmetricNonSquarePooling2dTest</a>(</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span> {</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  <span class="keywordflow">return</span> AsymmetricNonSquarePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span> }</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span> </div><div class="line"><a name="l01781"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a1a1e6dc70b7f1ca0c99fd6f0b48b4d48"> 1781</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a1a1e6dc70b7f1ca0c99fd6f0b48b4d48">AsymmetricNonSquarePooling2dUint8Test</a>(</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span> {</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  <span class="keywordflow">return</span> AsymmetricNonSquarePooling2dTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span> }</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span> </div><div class="line"><a name="l01788"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a866c67e5db471212f6ff29411aac0e8f"> 1788</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a866c67e5db471212f6ff29411aac0e8f">AsymmetricNonSquarePooling2dInt16Test</a>(</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span> {</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  <span class="keywordflow">return</span> AsymmetricNonSquarePooling2dTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span> }</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span> </div><div class="line"><a name="l01795"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a694dbeb3a87d65cd3cb854b5ced22a5b"> 1795</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a694dbeb3a87d65cd3cb854b5ced22a5b">ComparePooling2dTest</a>(</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType)</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span> {</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  <span class="keywordflow">return</span> ComparePooling2dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  workloadFactory, memoryManager, refWorkloadFactory, poolingType);</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span> }</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span> </div><div class="line"><a name="l01805"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#a7e5faed333caf71f1a19839308368046"> 1805</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#a7e5faed333caf71f1a19839308368046">ComparePooling2dUint8Test</a>(</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType)</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span> {</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  <span class="keywordflow">return</span> ComparePooling2dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  workloadFactory, memoryManager, refWorkloadFactory, poolingType, 0.1f, 128);</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span> }</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span> </div><div class="line"><a name="l01815"></a><span class="lineno"><a class="line" href="_pooling2d_test_impl_8hpp.html#ad67e6517d14f15abee1d159e89deb5fd"> 1815</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_pooling2d_test_impl_8cpp.html#ad67e6517d14f15abee1d159e89deb5fd">ComparePooling2dInt16Test</a>(</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  <a class="code" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType)</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span> {</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <span class="keywordflow">return</span> ComparePooling2dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  workloadFactory, memoryManager, refWorkloadFactory, poolingType);</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span> }</div><div class="ttc" id="src_2backends_2backends_common_2_workload_info_8hpp_html"><div class="ttname"><a href="src_2backends_2backends_common_2_workload_info_8hpp.html">WorkloadInfo.hpp</a></div></div> |