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  84 <div class="title">ActivationTestImpl.cpp</div>  </div>
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  87 <a href="_activation_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>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_activation_test_impl_8hpp.html">ActivationTestImpl.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_quantize_helper_8hpp.html">QuantizeHelper.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_resolve_type_8hpp.html">ResolveType.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;</div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;</div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_activation_fixture_8hpp.html">backendsCommon/test/ActivationFixture.hpp</a>&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_copy_utils_8hpp.html">backendsCommon/test/TensorCopyUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_test_utils_8hpp.html">backendsCommon/test/WorkloadTestUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;</div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_helpers_8hpp.html">test/TensorHelpers.hpp</a>&gt;</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;</div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &lt;boost/multi_array.hpp&gt;</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00023"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.html#aaa4e43f7b9a9b14145accdc347bc0e18">   23</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#aaa4e43f7b9a9b14145accdc347bc0e18">BoundedReLuTestCommon</a>(</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;    <span class="keywordtype">float</span> upperBound,</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;    <span class="keywordtype">float</span> lowerBound,</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;    <span class="keywordtype">float</span> inputScale,</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;    int32_t inputOffset,</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;    <span class="keywordtype">float</span> outputScale,</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;    int32_t outputOffset,</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;    <span class="keyword">const</span> std::vector&lt;T&gt;&amp; inputData,</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;    <span class="keyword">const</span> std::vector&lt;T&gt;&amp; outputExpectedData,</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth,</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight,</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels,</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize)</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;{</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;    boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    {</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;        inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(inputScale);</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;        inputTensorInfo.SetQuantizationOffset(inputOffset);</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;        outputTensorInfo.SetQuantizationScale(outputScale);</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;        outputTensorInfo.SetQuantizationOffset(outputOffset);</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    }</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</a> result(inputTensorInfo);</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, inputData);</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    <span class="comment">// Setup bounded ReLu.</span></div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    <a class="code" href="structarmnn_1_1_activation_queue_descriptor.html">armnn::ActivationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = upperBound;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = lowerBound;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(descriptor, workloadInfo);</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    inputHandle-&gt;Allocate();</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    outputHandle-&gt;Allocate();</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    workload-&gt;Execute();</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;result.<a class="code" href="struct_layer_test_result.html#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    result.<a class="code" href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 4&gt;(outputTensorInfo, outputExpectedData);</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;}</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a418191b7e7caba8173206c0870bc3684">   91</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a418191b7e7caba8173206c0870bc3684">BoundedReLuUpperAndLowerBoundTest</a>(</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;{</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 4u;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 5u;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    std::vector&lt;float&gt; input = std::vector&lt;float&gt;{</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;      -2.0f,       0.1f,     0.5f,     1.25f,</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;     0.786f,    0.9875f,    -1.5f,    0.384f,</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    1.0001f,       3.5f,     7.5f,    0.896f,</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;     2.126f,       2.0f,     0.3f,     0.15f,</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;     0.999f,       1.2f,    0.89f,      6.1f,</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    };</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    std::vector&lt;float&gt; output = std::vector&lt;float&gt;{</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;      -1.0f,       0.1f,     0.5f,      1.0f,</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;     0.786f,    0.9875f,    -1.0f,    0.384f,</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;       1.0f,       1.0f,     1.0f,    0.896f,</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;       1.0f,       1.0f,     0.3f,     0.15f,</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;     0.999f,       1.0f,    0.89f,      1.0f,</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    };</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="keywordflow">return</span> BoundedReLuTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;        workloadFactory, memoryManager, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output,</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;        inputWidth, inputHeight, inputChannels, inputBatchSize);</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;}</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a359c1f734f9da1d6459e9d878e5612ba">  122</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a359c1f734f9da1d6459e9d878e5612ba">BoundedReLuUpperBoundOnlyTest</a>(</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;{</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 4u;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 5u;</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    std::vector&lt;float&gt; input = std::vector&lt;float&gt;{</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;      -1.0f,       0.1f,     0.5f,      6.25f,</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;     0.786f,    5.9875f,    -0.5f,     0.384f,</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    6.0001f,       3.5f,     7.5f,     0.896f,</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;     2.126f,      12.0f,     0.3f,      0.15f,</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;     0.999f,       1.2f,    0.89f,       6.1f,</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    };</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    std::vector&lt;float&gt; output = std::vector&lt;float&gt;{</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;       0.0f,       0.1f,     0.5f,       6.0f,</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;     0.786f,    5.9875f,     0.0f,     0.384f,</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;       6.0f,       3.5f,     6.0f,     0.896f,</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;     2.126f,       6.0f,     0.3f,      0.15f,</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;     0.999f,       1.2f,    0.89f,       6.0f,</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    };</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    <span class="keywordflow">return</span> BoundedReLuTestCommon&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        workloadFactory, memoryManager, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output,</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;        inputWidth, inputHeight, inputChannels, inputBatchSize);</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;}</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;</div><div class="line"><a name="l00153"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a7aa10bded0d26089e0bc4333ada10064">  153</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a7aa10bded0d26089e0bc4333ada10064">BoundedReLuUint8UpperBoundOnlyTest</a>(</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;{</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth     = 3u;</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight    = 2u;</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels  = 1u;</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    std::vector&lt;uint8_t&gt; input = std::vector&lt;uint8_t&gt;{</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;         51, 124, 28,</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        251,   8, 92</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    };</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    std::vector&lt;uint8_t&gt; output = std::vector&lt;uint8_t&gt;{</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;          0, 122,  0,</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        255,   0, 58</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    };</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <span class="keywordtype">float</span> inputScale     = 12.0f / 255.0f;</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    int32_t inputOffset  = 63;</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <span class="keywordtype">float</span> outputScale    = 6.0f / 255.0f;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    int32_t outputOffset = 0;</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    <span class="keywordflow">return</span> BoundedReLuTestCommon&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;        workloadFactory, memoryManager, 6.0f, 0.0f,</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        inputScale, inputOffset, outputScale, outputOffset,</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        input, output, inputWidth, inputHeight, inputChannels, inputBatchSize);</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;}</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a5b674a831a483affefe085d350094b8b">  184</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a0e868e8fa03ce4c4674b007eae5dc1a2">BoundedReLuUint8UpperAndLowerBoundTest</a>(</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;{</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth     = 3u;</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight    = 2u;</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels  = 1u;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    std::vector&lt;uint8_t&gt; input = std::vector&lt;uint8_t&gt;{</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;         51, 230, 28,</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        251,   8, 92</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    };</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    std::vector&lt;uint8_t&gt; output = std::vector&lt;uint8_t&gt;{</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;         51, 192, 32,</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;        192,  32, 92</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    };</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    int32_t inputOffset = 112;</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    <span class="keywordtype">float</span> inputScale    = 0.0125f;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    <span class="keywordflow">return</span> BoundedReLuTestCommon&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        workloadFactory, memoryManager, 1.0f, -1.0f,</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        inputScale, inputOffset, inputScale, inputOffset, <span class="comment">// Input/output scale &amp; offset same.</span></div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        input, output, inputWidth, inputHeight, inputChannels, inputBatchSize);</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;}</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;{</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;<span class="keyword">struct </span>BoundedReLuRandomInputTestTraits</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;{</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 31u;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 19u;</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 4u;</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 2;</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.html#ae52296dff1f4879854f320d59f92574e">GetInputTensorInfo</a>()</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    {</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({ inputBatchSize, inputChannels, inputHeight, inputWidth },</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;            <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    }</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> GetOutputTensorInfo()</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    {</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({ outputBatchSize, outputChannels, outputHeight, outputWidth },</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;            <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    }</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;};</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;boost::multi_array&lt;float, 4&gt; BoundedReLuRandomInputTest(</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <span class="keywordtype">float</span> lowerBound,</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    <span class="keywordtype">float</span> upperBound,</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a>&amp; activationDescriptor)</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;{</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn.html#ae52296dff1f4879854f320d59f92574e">BoundedReLuRandomInputTestTraits::GetInputTensorInfo</a>();</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo();</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    boost::multi_array&lt;float, 4&gt; output(GetTensorShapeAsArray&lt;4&gt;(outputTensorInfo));</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    <span class="comment">// Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu</span></div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    <span class="comment">// range [lowerBound, upperBound].</span></div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    <span class="keyword">auto</span> input = MakeRandomTensor&lt;float, 4&gt;(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f);</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    <span class="comment">// Set up bounded ReLu.</span></div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <a class="code" href="structarmnn_1_1_activation_queue_descriptor.html">armnn::ActivationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = activationDescriptor;</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(descriptor, workloadInfo);</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    inputHandle-&gt;Allocate();</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    outputHandle-&gt;Allocate();</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    workload-&gt;Execute();</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    <span class="keywordflow">return</span> output;</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;}</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;</div><div class="line"><a name="l00284"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a7aaeeaa0a8683fae56caa66849228a87">  284</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a7aaeeaa0a8683fae56caa66849228a87">CompareBoundedReLuTest</a>(</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; refWorkloadFactory,</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <span class="keywordtype">float</span> upperBound,</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <span class="keywordtype">float</span> lowerBound)</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;{</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 4&gt;</a> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo());</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    <a class="code" href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = upperBound;</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = lowerBound;</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    result.<a class="code" href="struct_layer_test_result.html#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a> = BoundedReLuRandomInputTest(</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;        workloadFactory, memoryManager, 0.0f, upperBound, activationDescriptor);</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    result.<a class="code" href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = BoundedReLuRandomInputTest(</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;        refWorkloadFactory, <span class="keyword">nullptr</span>, 0.0f, upperBound, activationDescriptor);</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;}</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00307"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.html#a22562086e72d244fd7cf4156b958c134">  307</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T,4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a22562086e72d244fd7cf4156b958c134">ConstantLinearActivationTestCommon</a>(</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    int32_t qOffset = 0)</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;{</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight    = 20;</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth     = 17;</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels  = 3;</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize      = 5;</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[]  = {batchSize, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, shape, ArmnnType);</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, shape, ArmnnType);</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    {</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;        inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;        inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;        outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;        outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    }</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    <span class="comment">// Do linear activation that should leave the tensor unchanged.</span></div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    <a class="code" href="structarmnn_1_1_activation_queue_descriptor.html">armnn::ActivationQueueDescriptor</a> data;</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00344"><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ss="lineno">  371</span>&#160;{</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;    <span class="keywordflow">return</span> ConstantLinearActivationTestCommon&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager);</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;}</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;</div><div class="line"><a name="l00375"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a34b322827b0d8ff9f8b3b8fb9410f7d3">  375</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a34b322827b0d8ff9f8b3b8fb9410f7d3">ConstantLinearActivationUint8Test</a>(</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;{</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    <span class="keywordflow">return</span> ConstantLinearActivationTestCommon&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        workloadFactory, memoryManager, 4.0f, 3);</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;}</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;</div><div class="line"><a name="l00383"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a32a6595835f4cb5e93fec4182ada51bc">  383</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a32a6595835f4cb5e93fec4182ada51bc">ConstantLinearActivationInt16Test</a>(</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;{</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    <span class="keywordflow">return</span> ConstantLinearActivationTestCommon&lt;armnn::DataType::QSymmS16&gt;(</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;            workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;}</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00392"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.html#aaeea20fa5e5934ea49b8f764526a2d98">  392</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#aaeea20fa5e5934ea49b8f764526a2d98">SimpleActivationTest</a>(</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;    <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> activationFunction,</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    <span class="keywordtype">float</span> activationParameterA,</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    <span class="keywordtype">float</span> activationParameterB,</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    int32_t offset,</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt;&amp; inputData,</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;    <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    int32_t outOffset,</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt;&amp; outputExpectedData)</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;{</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16u;</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1u;</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1u;</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    <span class="keywordflow">if</span>(armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;    {</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(scale);</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        inputTensorInfo.SetQuantizationOffset(offset);</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        outputTensorInfo.SetQuantizationScale(outScale);</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        outputTensorInfo.SetQuantizationOffset(outOffset);</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;    }</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;    <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</a> result(inputTensorInfo);</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, armnnUtils::QuantizedVector&lt;T&gt;(inputData, scale, offset));</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;    <span class="comment">// Setup bounded ReLu.</span></div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;    <a class="code" href="structarmnn_1_1_activation_queue_descriptor.html">armnn::ActivationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = activationFunction;</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a> = activationParameterA;</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = activationParameterB;</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(descriptor, workloadInfo);</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;    inputHandle-&gt;Allocate();</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;    outputHandle-&gt;Allocate();</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0][0][0]);</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    workload-&gt;Execute();</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;result.<a class="code" href="struct_layer_test_result.html#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;    result.<a class="code" href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> =</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        MakeTensor&lt;T, 4&gt;(outputTensorInfo, armnnUtils::QuantizedVector&lt;T&gt;(outputExpectedData, outScale, outOffset));</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;}</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00464"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.html#a1020322feb8c6fe89ced59fcca8277c4">  464</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a1020322feb8c6fe89ced59fcca8277c4">SimpleSigmoidTestCommon</a>(</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;    <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;    <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    int32_t qOffset)</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;{</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    std::vector&lt;float&gt; inputData =</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    {</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;        0.1f,  0.2f,  0.3f,  0.4f,</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;        -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;        1.0f,  2.0f,  3.0f,  4.0f</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;    };</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    {</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        <span class="keywordflow">return</span> 1.0f / (1.0f + std::exp(-value));</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    };</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    std::vector&lt;float&gt; outputExpectedData(inputData.size());</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;    <span class="keywordflow">return</span> SimpleActivationTest&lt;ArmnnType&gt;(workloadFactory,</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;                                           memoryManager,</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;                                           <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</a>,</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;                                           0.f,</div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><span class="lineno">  858</span>&#160;            1.0f,  2.0f,  3.0f,  4.0f,</div><div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;            1.0f,  2.0f,  3.0f,  4.0f</div><div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;    };</div><div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160;</div><div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160;    <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00863"></a><span class="lineno">  863</span>&#160;    <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160;    {</div><div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;        <span class="keywordflow">return</span> std::sqrt(value);</div><div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160;    };</div><div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;    std::vector&lt;float&gt; outputExpectedData(inputData.size());</div><div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;</div><div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;    <span class="keywordflow">return</span> SimpleActivationTest&lt;ArmnnType&gt;(workloadFactory,</div><div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;                                           memoryManager,</div><div class="line"><a name="l00872"></a><span class="lineno">  872</span>&#160;                                           <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::ActivationFunction::Sqrt</a>,</div><div class="line"><a name="l00873"></a><span class="lineno">  873</span>&#160;                                           0.f,</div><div class="line"><a name="l00874"></a><span class="lineno">  874</span>&#160;                                           0.f,</div><div class="line"><a name="l00875"></a><span class="lineno">  875</span>&#160;                                           qScale,</div><div class="line"><a name="l00876"></a><span class="lineno">  876</span>&#160;                                           qOffset,</div><div class="line"><a name="l00877"></a><span class="lineno">  877</span>&#160;                                           inputData,</div><div class="line"><a name="l00878"></a><span class="lineno">  878</span>&#160;                                           qScale,</div><div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160;                                           qOffset,</div><div class="line"><a name="l00880"></a><span class="lineno">  880</span>&#160;                                           outputExpectedData);</div><div class="line"><a name="l00881"></a><span class="lineno">  881</span>&#160;}</div><div class="line"><a name="l00882"></a><span class="lineno">  882</span>&#160;</div><div class="line"><a name="l00883"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#ad3928f2c56ed15642ff6306cc6823ebd">  883</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#ad3928f2c56ed15642ff6306cc6823ebd">SqrtTest</a>(</div><div class="line"><a name="l00884"></a><span class="lineno">  884</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00885"></a><span class="lineno">  885</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00886"></a><span class="lineno">  886</span>&#160;{</div><div class="line"><a name="l00887"></a><span class="lineno">  887</span>&#160;    <span class="keywordflow">return</span> SqrtTestCommon&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00888"></a><span class="lineno">  888</span>&#160;}</div><div class="line"><a name="l00889"></a><span class="lineno">  889</span>&#160;</div><div class="line"><a name="l00890"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a6403e38cfee03672c164e3cba9863147">  890</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;uint8_t, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a6403e38cfee03672c164e3cba9863147">SqrtUint8Test</a>(</div><div class="line"><a name="l00891"></a><span class="lineno">  891</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00892"></a><span class="lineno">  892</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00893"></a><span class="lineno">  893</span>&#160;{</div><div class="line"><a name="l00894"></a><span class="lineno">  894</span>&#160;    <span class="keywordflow">return</span> SqrtTestCommon&lt;armnn::DataType::QAsymmU8&gt;(workloadFactory, memoryManager, 0.0625f, 64);</div><div class="line"><a name="l00895"></a><span class="lineno">  895</span>&#160;}</div><div class="line"><a name="l00896"></a><span class="lineno">  896</span>&#160;</div><div class="line"><a name="l00897"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.html#a8b855f5d3e8aab93decfa2bed46fc4cf">  897</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;int16_t, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a8b855f5d3e8aab93decfa2bed46fc4cf">SqrtInt16Test</a>(</div><div class="line"><a name="l00898"></a><span class="lineno">  898</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00899"></a><span class="lineno">  899</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00900"></a><span class="lineno">  900</span>&#160;{</div><div class="line"><a name="l00901"></a><span class="lineno">  901</span>&#160;    <span class="keywordflow">return</span> SqrtTestCommon&lt;armnn::DataType::QSymmS16&gt;(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00902"></a><span class="lineno">  902</span>&#160;}</div><div class="line"><a name="l00903"></a><span class="lineno">  903</span>&#160;</div><div class="line"><a name="l00904"></a><span class="lineno">  904</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00905"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.html#a26032da34ce1e283ae30d05ea3bbb103">  905</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</a> <a class="code" href="_activation_test_impl_8cpp.html#a26032da34ce1e283ae30d05ea3bbb103">SquareTestCommon</a>(</div><div class="line"><a name="l00906"></a><span class="lineno">  906</span>&#160;        <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00907"></a><span class="lineno">  907</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00908"></a><span class="lineno">  908</span>&#160;        <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00909"></a><span class="lineno">  909</span>&#160;        int32_t qOffset)</div><div class="line"><a name="l00910"></a><span class="lineno">  910</span>&#160;{</div><div class="line"><a name="l00911"></a><span class="lineno">  911</span>&#160;    std::vector&lt;float&gt; inputData = {</div><div class="line"><a name="l00912"></a><span class="lineno">  912</span>&#160;            -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00913"></a><span class="lineno">  913</span>&#160;            0.1f,  0.2f,  0.3f,  0.4f,</div><div class="line"><a name="l00914"></a><span class="lineno">  914</span>&#160;            -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00915"></a><span class="lineno">  915</span>&#160;            1.0f,  2.0f,  3.0f,  4.0f</div><div class="line"><a name="l00916"></a><span class="lineno">  916</span>&#160;    };</div><div class="line"><a name="l00917"></a><span class="lineno">  917</span>&#160;</div><div class="line"><a name="l00918"></a><span class="lineno">  918</span>&#160;    <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00919"></a><span class="lineno">  919</span>&#160;    <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00920"></a><span class="lineno">  920</span>&#160;    {</div><div class="line"><a name="l00921"></a><span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5"></a><span class="lineno"> 1075</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>&#160;</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>&#160;    std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>&#160;</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>&#160;    <a class="code" href="structarmnn_1_1_activation_queue_descriptor.html">armnn::ActivationQueueDescriptor</a> data;</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>&#160;    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>&#160;    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>&#160;    data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">m_A</a>        = a;</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>&#160;    data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">m_B</a>        = b;</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>&#160;    data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> = f;</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>&#160;</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>&#160;    <a class="code" href="structarmnn_1_1_activation_queue_descriptor.html">armnn::ActivationQueueDescriptor</a> refData = data;</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>&#160;    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>&#160;    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>&#160;</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>&#160;    std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(data, info);</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>&#160;    BOOST_ASSERT(workload != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>&#160;    std::unique_ptr&lt;armnn::IWorkload&gt; workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(refData, refInfo);</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>&#160;    BOOST_ASSERT(workloadRef != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>&#160;</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>&#160;    inputHandle-&gt;Allocate();</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>&#160;    outputHandle-&gt;Allocate();</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>&#160;    inputHandleRef-&gt;Allocate();</div><div class="line"><a name="l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  88 <div class="ttc" id="_activation_test_impl_8cpp_html_a6e45714708a6daf8688ef6ca58e54827"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a6e45714708a6daf8688ef6ca58e54827">LeakyReLuTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; LeakyReLuTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00680">ActivationTestImpl.cpp:680</a></div></div>
  89 <div class="ttc" id="structarmnn_1_1_activation_descriptor_html_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html#a017b2990003a014234f13e999dc7c689">armnn::ActivationDescriptor::m_A</a></div><div class="ttdeci">float m_A</div><div class="ttdoc">Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00037">Descriptors.hpp:37</a></div></div>
  90 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::ActivationFunction::Sqrt</a></div></div>
  91 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">armnn::ActivationFunction::LeakyReLu</a></div></div>
  92 <div class="ttc" id="_activation_test_impl_8cpp_html_aa87c451f7a773fd4ec9cdf11c20d7a58"><div class="ttname"><a href="_activation_test_impl_8cpp.html#aa87c451f7a773fd4ec9cdf11c20d7a58">SimpleSigmoidTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; SimpleSigmoidTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00499">ActivationTestImpl.cpp:499</a></div></div>
  93 <div class="ttc" id="_activation_test_impl_8cpp_html_a34b322827b0d8ff9f8b3b8fb9410f7d3"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a34b322827b0d8ff9f8b3b8fb9410f7d3">ConstantLinearActivationUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; ConstantLinearActivationUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00375">ActivationTestImpl.cpp:375</a></div></div>
  94 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">armnn::ActivationFunction::Square</a></div></div>
  95 <div class="ttc" id="classarmnn_1_1_i_workload_factory_html"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.html#l00021">WorkloadFactory.hpp:21</a></div></div>
  96 <div class="ttc" id="_activation_test_impl_8cpp_html_af226f71b992ee8076a3880def72b1f3f"><div class="ttname"><a href="_activation_test_impl_8cpp.html#af226f71b992ee8076a3880def72b1f3f">CompareActivationTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; CompareActivationTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::IWorkloadFactory &amp;refWorkloadFactory, armnn::ActivationFunction f, unsigned int batchSize)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l01115">ActivationTestImpl.cpp:1115</a></div></div>
  97 <div class="ttc" id="_tensor_copy_utils_8cpp_html_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.html#l00009">TensorCopyUtils.cpp:9</a></div></div>
  98 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">armnn::ActivationFunction::ReLu</a></div></div>
  99 <div class="ttc" id="_activation_test_impl_8cpp_html_af9293a4d81453abbe8cbdc788c290943"><div class="ttname"><a href="_activation_test_impl_8cpp.html#af9293a4d81453abbe8cbdc788c290943">LeakyReLuUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; LeakyReLuUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00722">ActivationTestImpl.cpp:722</a></div></div>
 100 <div class="ttc" id="_activation_test_impl_8cpp_html_a52af2639a8f96fbbc86343ea8914033a"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a52af2639a8f96fbbc86343ea8914033a">ConstantLinearActivationTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; ConstantLinearActivationTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00368">ActivationTestImpl.cpp:368</a></div></div>
 101 <div class="ttc" id="_activation_test_impl_8cpp_html_abe9073d08e150e3dd5e156af7ea8faa5"><div class="ttname"><a href="_activation_test_impl_8cpp.html#abe9073d08e150e3dd5e156af7ea8faa5">TanhUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; TanhUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l01004">ActivationTestImpl.cpp:1004</a></div></div>
 102 <div class="ttc" id="_activation_test_impl_8cpp_html_aaa4e43f7b9a9b14145accdc347bc0e18"><div class="ttname"><a href="_activation_test_impl_8cpp.html#aaa4e43f7b9a9b14145accdc347bc0e18">BoundedReLuTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; BoundedReLuTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float upperBound, float lowerBound, float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset, const std::vector&lt; T &gt; &amp;inputData, const std::vector&lt; T &gt; &amp;outputExpectedData, unsigned int inputWidth, unsigned int inputHeight, unsigned int inputChannels, unsigned int inputBatchSize)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00023">ActivationTestImpl.cpp:23</a></div></div>
 103 <div class="ttc" id="_activation_test_impl_8cpp_html_a8b855f5d3e8aab93decfa2bed46fc4cf"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a8b855f5d3e8aab93decfa2bed46fc4cf">SqrtInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; SqrtInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00897">ActivationTestImpl.cpp:897</a></div></div>
 104 <div class="ttc" id="namespacearmnn_html_ae52296dff1f4879854f320d59f92574e"><div class="ttname"><a href="namespacearmnn.html#ae52296dff1f4879854f320d59f92574e">armnn::GetInputTensorInfo</a></div><div class="ttdeci">TensorInfo GetInputTensorInfo(const Network *network)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.html#l00337">QuantizerTest.cpp:337</a></div></div>
 105 <div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_html_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00049">WorkloadData.hpp:49</a></div></div>
 106 <div class="ttc" id="_activation_test_impl_8cpp_html_a7aaeeaa0a8683fae56caa66849228a87"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a7aaeeaa0a8683fae56caa66849228a87">CompareBoundedReLuTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; CompareBoundedReLuTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::IWorkloadFactory &amp;refWorkloadFactory, float upperBound, float lowerBound)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00284">ActivationTestImpl.cpp:284</a></div></div>
 107 <div class="ttc" id="_activation_test_impl_8cpp_html_a359c1f734f9da1d6459e9d878e5612ba"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a359c1f734f9da1d6459e9d878e5612ba">BoundedReLuUpperBoundOnlyTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00122">ActivationTestImpl.cpp:122</a></div></div>
 108 <div class="ttc" id="_activation_test_impl_8cpp_html_a58872a37a87790e3a3f91ee254ce304a"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a58872a37a87790e3a3f91ee254ce304a">ReLuTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; ReLuTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00570">ActivationTestImpl.cpp:570</a></div></div>
 109 <div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div>
 110 <div class="ttc" id="classarmnn_1_1_i_workload_factory_html_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
 111 <div class="ttc" id="structarmnn_1_1_activation_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00020">Descriptors.hpp:20</a></div></div>
 112 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00054">Types.hpp:54</a></div></div>
 113 <div class="ttc" id="struct_layer_test_result_html"><div class="ttname"><a href="struct_layer_test_result.html">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.html#l00029">LayerTestResult.hpp:29</a></div></div>
 114 <div class="ttc" id="_tensor_copy_utils_8cpp_html_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.html#l00014">TensorCopyUtils.cpp:14</a></div></div>
 115 <div class="ttc" id="_activation_test_impl_8cpp_html_aab2458914aa40f83ba027de7a8c07d06"><div class="ttname"><a href="_activation_test_impl_8cpp.html#aab2458914aa40f83ba027de7a8c07d06">SqrtTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; SqrtTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00849">ActivationTestImpl.cpp:849</a></div></div>
 116 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::ActivationFunction::Abs</a></div></div>
 117 <div class="ttc" id="_activation_test_impl_8cpp_html_addef260aaa3c7f7f1d08f821b823af33"><div class="ttname"><a href="_activation_test_impl_8cpp.html#addef260aaa3c7f7f1d08f821b823af33">CompareActivationUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; CompareActivationUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::IWorkloadFactory &amp;refWorkloadFactory, armnn::ActivationFunction f)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l01126">ActivationTestImpl.cpp:1126</a></div></div>
 118 <div class="ttc" id="classarmnn_1_1_i_backend_internal_html_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr&lt; IMemoryManager &gt; IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.html#l00090">IBackendInternal.hpp:90</a></div></div>
 119 <div class="ttc" id="_activation_test_impl_8cpp_html_a20b01cc1552ab2c3abd70166fdd35faf"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a20b01cc1552ab2c3abd70166fdd35faf">ReLuInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; ReLuInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00555">ActivationTestImpl.cpp:555</a></div></div>
 120 <div class="ttc" id="struct_layer_test_result_html_ac9d44d346bb7c89f7a7aa31d2bee947f"><div class="ttname"><a href="struct_layer_test_result.html#ac9d44d346bb7c89f7a7aa31d2bee947f">LayerTestResult::output</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; output</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.html#l00040">LayerTestResult.hpp:40</a></div></div>
 121 <div class="ttc" id="_activation_test_impl_8cpp_html_a4b43ab0b58fc8d4ad51b1b71c0e35622"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a4b43ab0b58fc8d4ad51b1b71c0e35622">SoftReLuTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; SoftReLuTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00624">ActivationTestImpl.cpp:624</a></div></div>
 122 <div class="ttc" id="_activation_test_impl_8cpp_html_ae42bb4023d8578a27159c95dd4b33b28"><div class="ttname"><a href="_activation_test_impl_8cpp.html#ae42bb4023d8578a27159c95dd4b33b28">BoundedReLuInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; BoundedReLuInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00614">ActivationTestImpl.cpp:614</a></div></div>
 123 <div class="ttc" id="_activation_test_impl_8cpp_html_acf22306b81aa054c64c48730b2786f96"><div class="ttname"><a href="_activation_test_impl_8cpp.html#acf22306b81aa054c64c48730b2786f96">ReLuTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; ReLuTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00521">ActivationTestImpl.cpp:521</a></div></div>
 124 <div class="ttc" id="structarmnn_1_1_workload_info_html"><div class="ttname"><a href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.html#l00016">WorkloadInfo.hpp:16</a></div></div>
 125 <div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
 126 <div class="ttc" id="_activation_test_impl_8cpp_html_aa8c2d170a4b51447f575183cee9579ab"><div class="ttname"><a href="_activation_test_impl_8cpp.html#aa8c2d170a4b51447f575183cee9579ab">AbsTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; AbsTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00737">ActivationTestImpl.cpp:737</a></div></div>
 127 <div class="ttc" id="_resolve_type_8hpp_html"><div class="ttname"><a href="_resolve_type_8hpp.html">ResolveType.hpp</a></div></div>
 128 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</a></div></div>
 129 <div class="ttc" id="_activation_test_impl_8cpp_html_aacd820bdf2307a2aa667db2899283035"><div class="ttname"><a href="_activation_test_impl_8cpp.html#aacd820bdf2307a2aa667db2899283035">TanhInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; TanhInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l01011">ActivationTestImpl.cpp:1011</a></div></div>
 130 <div class="ttc" id="_activation_test_impl_8cpp_html_a6584d436388485a5bd9252430a0af5b6"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a6584d436388485a5bd9252430a0af5b6">SquareTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; SquareTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00939">ActivationTestImpl.cpp:939</a></div></div>
 131 <div class="ttc" id="_activation_test_impl_8cpp_html_a65aa329dc6abc6cf9dfb6177f42595de"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a65aa329dc6abc6cf9dfb6177f42595de">TanhTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; TanhTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00961">ActivationTestImpl.cpp:961</a></div></div>
 132 <div class="ttc" id="_activation_test_impl_8hpp_html"><div class="ttname"><a href="_activation_test_impl_8hpp.html">ActivationTestImpl.hpp</a></div></div>
 133 <div class="ttc" id="structarmnn_1_1_activation_descriptor_html_a28c4c9cb15f6be3499abbc46b356060b"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html#a28c4c9cb15f6be3499abbc46b356060b">armnn::ActivationDescriptor::m_B</a></div><div class="ttdeci">float m_B</div><div class="ttdoc">Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00039">Descriptors.hpp:39</a></div></div>
 134 <div class="ttc" id="_activation_fixture_8hpp_html"><div class="ttname"><a href="_activation_fixture_8hpp.html">ActivationFixture.hpp</a></div></div>
 135 <div class="ttc" id="_activation_test_impl_8cpp_html_a0e868e8fa03ce4c4674b007eae5dc1a2"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a0e868e8fa03ce4c4674b007eae5dc1a2">BoundedReLuUint8UpperAndLowerBoundTest</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00184">ActivationTestImpl.cpp:184</a></div></div>
 136 <div class="ttc" id="_activation_test_impl_8cpp_html_a26219b66822d57b9fcce7a2504d1fca6"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a26219b66822d57b9fcce7a2504d1fca6">SquareInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; SquareInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00953">ActivationTestImpl.cpp:953</a></div></div>
 137 <div class="ttc" id="classarmnn_1_1_tensor_info_html_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00259">Tensor.cpp:259</a></div></div>
 138 <div class="ttc" id="_activation_test_impl_8cpp_html_ac01b6901c3f2921c998aff77a8362f87"><div class="ttname"><a href="_activation_test_impl_8cpp.html#ac01b6901c3f2921c998aff77a8362f87">LeakyReLuInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; LeakyReLuInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00729">ActivationTestImpl.cpp:729</a></div></div>
 139 <div class="ttc" id="_activation_test_impl_8cpp_html_a6403e38cfee03672c164e3cba9863147"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a6403e38cfee03672c164e3cba9863147">SqrtUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; SqrtUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00890">ActivationTestImpl.cpp:890</a></div></div>
 140 <div class="ttc" id="_activation_test_impl_8cpp_html_a641db2befcd47ac97af966e20b1c4c2c"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a641db2befcd47ac97af966e20b1c4c2c">SoftReLuInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; SoftReLuInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00672">ActivationTestImpl.cpp:672</a></div></div>
 141 <div class="ttc" id="_activation_test_impl_8cpp_html_a923aa3e41cd11f5eeb7cc973fd8d3c76"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a923aa3e41cd11f5eeb7cc973fd8d3c76">TanhTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; TanhTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00997">ActivationTestImpl.cpp:997</a></div></div>
 142 <div class="ttc" id="struct_layer_test_result_html_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array&lt; T, n &gt; outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.html#l00041">LayerTestResult.hpp:41</a></div></div>
 143 <div class="ttc" id="_quantize_helper_8hpp_html"><div class="ttname"><a href="_quantize_helper_8hpp.html">QuantizeHelper.hpp</a></div></div>
 144 <div class="ttc" id="_activation_test_impl_8cpp_html_a1020322feb8c6fe89ced59fcca8277c4"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a1020322feb8c6fe89ced59fcca8277c4">SimpleSigmoidTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; SimpleSigmoidTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00464">ActivationTestImpl.cpp:464</a></div></div>
 145 <div class="ttc" id="_activation_test_impl_8cpp_html_aaeea20fa5e5934ea49b8f764526a2d98"><div class="ttname"><a href="_activation_test_impl_8cpp.html#aaeea20fa5e5934ea49b8f764526a2d98">SimpleActivationTest</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; SimpleActivationTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::ActivationFunction activationFunction, float activationParameterA, float activationParameterB, float scale, int32_t offset, const std::vector&lt; float &gt; &amp;inputData, float outScale, int32_t outOffset, const std::vector&lt; float &gt; &amp;outputExpectedData)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00392">ActivationTestImpl.cpp:392</a></div></div>
 146 <div class="ttc" id="_activation_test_impl_8cpp_html_a11baf4886951944fcf149e2a92197e58"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a11baf4886951944fcf149e2a92197e58">AbsUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; AbsUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00778">ActivationTestImpl.cpp:778</a></div></div>
 147 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef">armnn::ActivationFunction::SoftReLu</a></div></div>
 148 <div class="ttc" id="_workload_test_utils_8hpp_html"><div class="ttname"><a href="_workload_test_utils_8hpp.html">WorkloadTestUtils.hpp</a></div></div>
 149 <div class="ttc" id="_activation_test_impl_8cpp_html_a61fffaf40ad721073b70c350174d0ff3"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a61fffaf40ad721073b70c350174d0ff3">SquareUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; SquareUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00946">ActivationTestImpl.cpp:946</a></div></div>
 150 <div class="ttc" id="_activation_test_impl_8cpp_html_aa986502e638eba65543c1cbb01467d26"><div class="ttname"><a href="_activation_test_impl_8cpp.html#aa986502e638eba65543c1cbb01467d26">ReLuUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; ReLuUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00563">ActivationTestImpl.cpp:563</a></div></div>
 151 <div class="ttc" id="structarmnn_1_1_activation_descriptor_html_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00035">Descriptors.hpp:35</a></div></div>
 152 <div class="ttc" id="_activation_test_impl_8cpp_html_a86f53855f5ab422f4e035b1aa11676f8"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a86f53855f5ab422f4e035b1aa11676f8">SqrtNNTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 5 &gt; SqrtNNTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00792">ActivationTestImpl.cpp:792</a></div></div>
 153 <div class="ttc" id="classarmnn_1_1_i_workload_factory_html_a4458d75c0db21c6abc941cd93a6a24c5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.html#a4458d75c0db21c6abc941cd93a6a24c5">armnn::IWorkloadFactory::CreateActivation</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateActivation(const ActivationQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.html#l01082">WorkloadFactory.cpp:1082</a></div></div>
 154 <div class="ttc" id="_activation_test_impl_8cpp_html_a732229b22cff2a8f96798c38832cab92"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a732229b22cff2a8f96798c38832cab92">SoftReLuUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; SoftReLuUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00665">ActivationTestImpl.cpp:665</a></div></div>
 155 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a></div><div class="ttdoc">min(a, max(b, input)) </div></div>
 156 <div class="ttc" id="_activation_test_impl_8cpp_html_a8bfdab68fed1467b8720cceb47881236"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a8bfdab68fed1467b8720cceb47881236">SoftReLuTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; SoftReLuTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00658">ActivationTestImpl.cpp:658</a></div></div>
 157 <div class="ttc" id="_activation_test_impl_8cpp_html_a0889979f9ffb67b036c3928c6e94af50"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a0889979f9ffb67b036c3928c6e94af50">SimpleSigmoidUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; SimpleSigmoidUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00506">ActivationTestImpl.cpp:506</a></div></div>
 158 <div class="ttc" id="_activation_test_impl_8cpp_html_a7aa10bded0d26089e0bc4333ada10064"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a7aa10bded0d26089e0bc4333ada10064">BoundedReLuUint8UpperBoundOnlyTest</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 4 &gt; BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00153">ActivationTestImpl.cpp:153</a></div></div>
 159 <div class="ttc" id="_activation_test_impl_8cpp_html_a0758d9003f13b30d5e29eae6cd89c32b"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a0758d9003f13b30d5e29eae6cd89c32b">CompareActivationTestImpl</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; CompareActivationTestImpl(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::IWorkloadFactory &amp;refWorkloadFactory, armnn::ActivationFunction f, unsigned int batchSize=5, float qScale=0.0f, int32_t qOffset=0)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l01021">ActivationTestImpl.cpp:1021</a></div></div>
 160 <div class="ttc" id="_activation_test_impl_8cpp_html_a32a6595835f4cb5e93fec4182ada51bc"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a32a6595835f4cb5e93fec4182ada51bc">ConstantLinearActivationInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; ConstantLinearActivationInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00383">ActivationTestImpl.cpp:383</a></div></div>
 161 <div class="ttc" id="classarmnn_1_1_tensor_info_html_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00275">Tensor.cpp:275</a></div></div>
 162 <div class="ttc" id="_activation_test_impl_8cpp_html_ad3928f2c56ed15642ff6306cc6823ebd"><div class="ttname"><a href="_activation_test_impl_8cpp.html#ad3928f2c56ed15642ff6306cc6823ebd">SqrtTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; SqrtTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00883">ActivationTestImpl.cpp:883</a></div></div>
 163 <div class="ttc" id="_activation_test_impl_8cpp_html_a0120909fa6b3032270399355f14654de"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a0120909fa6b3032270399355f14654de">LeakyReLuTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; LeakyReLuTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00715">ActivationTestImpl.cpp:715</a></div></div>
 164 <div class="ttc" id="_activation_test_impl_8cpp_html_a22562086e72d244fd7cf4156b958c134"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a22562086e72d244fd7cf4156b958c134">ConstantLinearActivationTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; ConstantLinearActivationTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale=0.0f, int32_t qOffset=0)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00307">ActivationTestImpl.cpp:307</a></div></div>
 165 <div class="ttc" id="_tensor_copy_utils_8hpp_html"><div class="ttname"><a href="_tensor_copy_utils_8hpp.html">TensorCopyUtils.hpp</a></div></div>
 166 <div class="ttc" id="_tensor_helpers_8hpp_html"><div class="ttname"><a href="_tensor_helpers_8hpp.html">TensorHelpers.hpp</a></div></div>
 167 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</a></div></div>
 168 <div class="ttc" id="_activation_test_impl_8cpp_html_a31872d5729b4d7734c1eb0d189a0eece"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a31872d5729b4d7734c1eb0d189a0eece">AbsTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; AbsTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00771">ActivationTestImpl.cpp:771</a></div></div>
 169 <div class="ttc" id="_activation_test_impl_8cpp_html_a6558a4306d758625ab7804e9cb70b058"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a6558a4306d758625ab7804e9cb70b058">SimpleSigmoidInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; SimpleSigmoidInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00513">ActivationTestImpl.cpp:513</a></div></div>
 170 <div class="ttc" id="structarmnn_1_1_activation_queue_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_activation_queue_descriptor.html">armnn::ActivationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00130">WorkloadData.hpp:130</a></div></div>
 171 <div class="ttc" id="namespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b">armnn::ActivationFunction::Linear</a></div></div>
 172 <div class="ttc" id="_activation_test_impl_8cpp_html_a8dd4b2ac72e85dcfeb8540b7d5649b47"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a8dd4b2ac72e85dcfeb8540b7d5649b47">AbsInt16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 4 &gt; AbsInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00785">ActivationTestImpl.cpp:785</a></div></div>
 173 <div class="ttc" id="_activation_test_impl_8cpp_html_a418191b7e7caba8173206c0870bc3684"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a418191b7e7caba8173206c0870bc3684">BoundedReLuUpperAndLowerBoundTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00091">ActivationTestImpl.cpp:91</a></div></div>
 174 <div class="ttc" id="_activation_test_impl_8cpp_html_a26032da34ce1e283ae30d05ea3bbb103"><div class="ttname"><a href="_activation_test_impl_8cpp.html#a26032da34ce1e283ae30d05ea3bbb103">SquareTestCommon</a></div><div class="ttdeci">LayerTestResult&lt; T, 4 &gt; SquareTestCommon(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.html#l00905">ActivationTestImpl.cpp:905</a></div></div>
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