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+<a href="_softmax_test_impl_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_softmax_test_impl_8hpp.xhtml">SoftmaxTestImpl.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_test_utils_8hpp.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;{</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="keyword">struct </span>Simple3dSoftmaxOutputData</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;{</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; outputData =</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; 0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; 0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; };</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputShape{ 1, 8, 1 };</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; inputData =</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; 0.0f, 1.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; 0.5f, 0.0f, 0.0f, 0.0f,</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;};</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;<span class="keyword">struct </span>Simple4dSoftmaxData</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;{</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputShape{ 1, 8, 1, 1 };</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; <span class="keyword">const</span> std::vector&lt;float&gt; outputData =</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; 0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; 0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; };</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; inputData =</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; 0.0f, 1.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; 0.5f, 0.0f, 0.0f, 0.0f</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; };</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;};</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;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, std::<span class="keywordtype">size_t</span> n, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, n&gt;</a> SimpleSoftmaxBaseTestImpl(</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>&amp; inputShape,</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; outputData,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; inputData,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="keywordtype">int</span> axis = 1)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;{</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="keyword">using</span> std::exp;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.f / 256.f;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> qOffset = 0;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</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; inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(inputShape, ArmnnType);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(inputShape, ArmnnType);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, n&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="comment">// Each row is independently softmax&#39;d.</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;T, n&gt;(inputTensorInfo, armnnUtils::QuantizedVector&lt;T&gt;(inputData, qScale, qOffset));</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; std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml">armnn::SoftmaxQueueDescriptor</a> data;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = beta;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">m_Axis</a> = axis;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a8a843d44d2e81df87e414df3b3e688de">CreateSoftmax</a>(data, info);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), input.origin());</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; BOOST_ASSERT(workload);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(ret.output.origin(), outputHandle.get());</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; std::vector&lt;T&gt; expectedOutput = armnnUtils::QuantizedVector&lt;T&gt;(outputData, qScale, qOffset);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; ret.outputExpected = MakeTensor&lt;T, n&gt;(outputTensorInfo, expectedOutput);</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; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;}</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</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="l00120"></a><span class="lineno"> 120</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> SimpleSoftmaxTestImpl(</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160;{</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keyword">using</span> std::exp;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputShape{ 2, 4 };</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; <span class="keywordtype">float</span> x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta),</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) };</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="keywordtype">float</span> sum0 = x0[0] + x0[1] + x0[2] + x0[3];</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; <span class="keywordtype">float</span> x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta),</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) };</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <span class="keywordtype">float</span> sum1 = x1[0] + x1[1] + x1[2] + x1[3];</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; outputData = { x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 };</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; <span class="keyword">const</span> std::vector&lt;float&gt; inputData =</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; 0.f, 1.f, 0.f, 0.f,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; .5f, 0.f, 0.f, 0.f,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; };</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="keywordflow">return</span> SimpleSoftmaxBaseTestImpl&lt;ArmnnType, 2&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; inputShape, outputData, inputData);</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="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="l00149"></a><span class="lineno"> 149</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> SimpleSoftmaxTestImpl(</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="keywordtype">int</span> axis)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;{</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputShape;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; std::vector&lt;float&gt; inputData;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; std::vector&lt;float&gt; outputData;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="keywordflow">switch</span> (axis)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <span class="keywordflow">case</span> -2:</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <span class="keywordflow">case</span> 0:</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; inputShape = {5, 2};</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; inputData =</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; 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; };</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; outputData =</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; 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; 0.087144312427294f,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; 7.246299848982885e-08f</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; };</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <span class="keywordflow">case</span> -1:</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; inputShape = {2, 5};</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; inputData =</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; {</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f</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;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; outputData =</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; 7.246299848982885e-08f,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; 7.246299848982885e-08f</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; };</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="keywordflow">break</span>;</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; }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; <span class="keywordflow">return</span> SimpleSoftmaxBaseTestImpl&lt;ArmnnType, 2&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; inputShape, outputData, inputData, axis);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160;}</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</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="l00204"></a><span class="lineno"> 204</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 3&gt;</a> Simple3dSoftmaxTestImpl(</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>&amp; inputShape,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; outputData,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; inputData,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; <span class="keywordtype">int</span> axis = 1)</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="keywordflow">return</span> SimpleSoftmaxBaseTestImpl&lt;ArmnnType, 3&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; inputShape, outputData, inputData, axis);</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;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</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="l00218"></a><span class="lineno"> 218</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 4&gt;</a> Simple4dSoftmaxTestImpl(</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>&amp; inputShape,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; outputData,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt;&amp; inputData,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; <span class="keywordtype">int</span> axis = 1)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160;{</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <span class="keywordflow">return</span> SimpleSoftmaxBaseTestImpl&lt;ArmnnType, 4&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; inputShape, outputData, inputData, axis);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160;}</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</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="l00233"></a><span class="lineno"> 233</span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> CompareSoftmaxTestImpl(</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; refWorkloadFactory,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; <span class="keywordtype">float</span> beta)</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; <span class="keyword">const</span> <span class="keywordtype">int</span> batchSize = 20;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> channels = 30;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { batchSize, channels };</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; inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, inputShape, ArmnnType);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, inputShape, ArmnnType);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <span class="keywordtype">float</span> qScale = 1.f / 256.f;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <span class="keywordtype">int</span> qOffset = 0;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;T, 2&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; <span class="keyword">auto</span> input = MakeRandomTensor&lt;T, 2&gt;(inputTensorInfo, 0xF00D, 0.0f, 1.0f);</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; std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml">armnn::SoftmaxQueueDescriptor</a> data;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = beta;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160;</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; <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml">armnn::SoftmaxQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a8a843d44d2e81df87e414df3b3e688de">CreateSoftmax</a>(data, info);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a8a843d44d2e81df87e414df3b3e688de">CreateSoftmax</a>(refData, refInfo);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; outputHandleRef-&gt;Allocate();</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; inputHandleRef-&gt;Allocate();</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; inputHandle-&gt;Allocate();</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &amp;input[0][0]);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &amp;input[0][0]);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; workloadRef-&gt;Execute();</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.output[0][0], outputHandle.get());</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;ret.outputExpected[0][0], outputHandleRef.get());</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160;}</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160;} <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160;</div><div class="line"><a name="l00304"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a4639248490da1500dd6e919f20c0e7d8"> 304</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,2&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a49081ef56cfc5fafad212dfbce4f259b">SimpleSoftmaxTest</a>(</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160;{</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; <span class="keywordflow">return</span> SimpleSoftmaxTestImpl&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager, beta);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160;}</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160;</div><div class="line"><a name="l00312"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#ac5d051940aaf87bdb39c1abd2622a002"> 312</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,2&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a493bef3f5fc0d657b0a5fe29e58dcbdf">SimpleAxisSoftmaxTest</a>(</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; <span class="keywordtype">int</span> axis)</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160;{</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; <span class="keywordflow">return</span> SimpleSoftmaxTestImpl&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager, beta, axis);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160;}</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160;</div><div class="line"><a name="l00321"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a16ab28decdb5577af507e4a2c90ac0d1"> 321</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,3&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a95c728251146e1f5bcddf8bd04927553">Simple3dSoftmaxTest</a>(</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160;{</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; Simple3dSoftmaxOutputData data;</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; <span class="keywordflow">return</span> Simple3dSoftmaxTestImpl&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; data.inputShape, data.outputData, data.inputData);</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;</div><div class="line"><a name="l00331"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a119922d6d12c318fa477aeea9377df0c"> 331</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,3&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a1f0c412bd42fe4ef6b408d463a7a438f">Simple3dAxisSoftmaxTest</a>(</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; <span class="keywordtype">int</span> axis)</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160;{</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputShape;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; std::vector&lt;float&gt; inputData;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; std::vector&lt;float&gt; outputData;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; <span class="keywordflow">switch</span> (axis)</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; {</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; <span class="keywordflow">case</span> -3:</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; <span class="keywordflow">case</span> 0:</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; {</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; inputShape = {5, 2, 2};</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; inputData =</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; };</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; outputData =</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; {</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160; 0.236882800924671f,</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f,</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; 0.087144312427294f,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160;</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; 0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f,</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; 0.032058600957022f,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; 7.246299848982885e-08f</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; };</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; }</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; <span class="keywordflow">case</span> -2:</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; {</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; 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7.246299848982885e-08f</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; };</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; }</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; <span class="keywordflow">case</span> -1:</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; {</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; inputShape = {2, 2, 5};</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; inputData =</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; {</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160; 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; };</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; outputData =</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; {</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; 7.246299848982885e-08f,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; 7.246299848982885e-08f,</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160;</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; 7.246299848982885e-08f,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; 7.246299848982885e-08f</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; };</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160; <span class="keywordflow">break</span>;</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; }</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; <span class="keywordflow">return</span> Simple3dSoftmaxTestImpl&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; inputShape, outputData, inputData, axis);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160;}</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160;</div><div class="line"><a name="l00425"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#aad3da96f8d9621d6d86772c8202fa4b7"> 425</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,4&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a0d366093ec6ca27079466c811151665c">Simple4dSoftmaxTest</a>(</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; <span class="keywordtype">float</span> beta)</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; Simple4dSoftmaxData data;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; <span class="keywordflow">return</span> Simple4dSoftmaxTestImpl&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager, beta, data.inputShape,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; data.outputData, data.inputData);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160;}</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160;</div><div class="line"><a name="l00435"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#adb1ea636bbcf4875367270ba657c1a24"> 435</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,4&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a1dc1b7112df501f911a501ad40589215">Simple4dAxisSoftmaxTest</a>(</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; <span class="keywordtype">float</span> beta,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; <span class="keywordtype">int</span> axis)</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; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputShape;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; std::vector&lt;float&gt; inputData;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; std::vector&lt;float&gt; outputData;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; <span class="keywordflow">switch</span> (axis)</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; <span class="keywordflow">case</span> -4:</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; <span class="keywordflow">case</span> 0:</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; {</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; inputShape = {5, 2, 2, 2};</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; inputData =</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; {</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; 17.0f, -1.0f, 17.0f, -1.0f, 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; 16.0f, -2.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f, 15.0f, -3.0f,</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; 15.0f, -3.0f, 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 14.0f, -4.0f,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f, 1.0f, -17.0f, 1.0f, -17.0f</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160; };</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160; outputData =</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; {</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; 0.643914213228014f,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.236882800924671f,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; 0.236882800924671f,</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.236882800924671f,</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; 0.236882800924671f,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; 0.236882800924671f, 0.087144312427294f, 0.087144312427294f, 0.087144312427294f,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; 0.087144312427294f,</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; 0.087144312427294f, 0.087144312427294f, 0.087144312427294f, 0.087144312427294f,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; 0.032058600957022f,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; 0.032058600957022f, 0.032058600957022f, 0.032058600957022f, 0.032058600957022f,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; 0.032058600957022f,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; 7.246299848982885e-08f,</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; 7.246299848982885e-08f, 7.246299848982885e-08f</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; };</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; <span class="keywordflow">break</span>;</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">case</span> -3:</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; {</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160; inputShape = {2, 5, 2, 2};</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; inputData =</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; {</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f,</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f,</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f,</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160; 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; };</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; outputData =</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; {</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; 0.236882800924671f,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; 0.087144312427294f,</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; 0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f,</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; 0.032058600957022f,</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; 7.246299848982885e-08f,</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160;</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160;</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; 0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160; 0.236882800924671f,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; 0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f,</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; 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memoryManager,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>&#160; beta,</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>&#160; data.inputShape,</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>&#160; data.outputData,</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>&#160; data.inputData);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>&#160;}</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>&#160;</div><div class="line"><a name="l00622"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#abbe2d209a74d323f9243c1943dfd429e"> 622</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t,4&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#abbe2d209a74d323f9243c1943dfd429e">Simple4dSoftmaxUint8Test</a>(</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>&#160; 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data.inputShape, data.outputData, data.inputData);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>&#160;}</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>&#160;</div><div class="line"><a name="l00633"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a28b9861c52ee758d11db282794b21306"> 633</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;armnn::Half,2&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a28b9861c52ee758d11db282794b21306">SimpleSoftmaxFloat16Test</a>(</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>&#160;{</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>&#160; <span class="keywordflow">return</span> SimpleSoftmaxTestImpl&lt;armnn::DataType::Float16&gt;(workloadFactory, memoryManager, beta);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>&#160;}</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>&#160;</div><div class="line"><a name="l00641"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a2c83aa074ca45a521e8253ddf61cdeb2"> 641</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;armnn::Half,3&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a2c83aa074ca45a521e8253ddf61cdeb2">Simple3dSoftmaxFloat16Test</a>(</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>&#160;{</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>&#160; Simple3dSoftmaxOutputData data;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>&#160; <span class="keywordflow">return</span> Simple3dSoftmaxTestImpl&lt;armnn::DataType::Float16&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>&#160; data.inputShape, data.outputData, data.inputData);</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>&#160;}</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>&#160;</div><div class="line"><a name="l00651"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a9457b55b18827c741d53f42e36d4a2d1"> 651</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;armnn::Half,4&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a9457b55b18827c741d53f42e36d4a2d1">Simple4dSoftmaxFloat16Test</a>(</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>&#160;{</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>&#160; Simple4dSoftmaxData data;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>&#160; <span class="keywordflow">return</span> Simple4dSoftmaxTestImpl&lt;armnn::DataType::Float16&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>&#160; data.inputShape, data.outputData, data.inputData);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>&#160;}</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>&#160;</div><div class="line"><a name="l00661"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a55ce19436218d7abbbc53b5f66c2b7fe"> 661</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t,2&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a55ce19436218d7abbbc53b5f66c2b7fe">SimpleSoftmaxUint16Test</a>(</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>&#160;{</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>&#160; <span class="keywordflow">return</span> SimpleSoftmaxTestImpl&lt;armnn::DataType::QSymmS16&gt;(workloadFactory, memoryManager, beta);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>&#160;}</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>&#160;</div><div class="line"><a name="l00669"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#abde790042baa52b75247c30559840cbf"> 669</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t,3&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#abde790042baa52b75247c30559840cbf">Simple3dSoftmaxUint16Test</a>(</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>&#160;{</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>&#160; Simple3dSoftmaxOutputData data;</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>&#160; <span class="keywordflow">return</span> Simple3dSoftmaxTestImpl&lt;armnn::DataType::QSymmS16&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>&#160; data.inputShape, data.outputData, data.inputData);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>&#160;}</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>&#160;</div><div class="line"><a name="l00679"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a418bae8de268bfed7d215c884c1373ea"> 679</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;int16_t,4&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a418bae8de268bfed7d215c884c1373ea">Simple4dSoftmaxUint16Test</a>(</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>&#160;{</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>&#160; Simple4dSoftmaxData data;</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>&#160;</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>&#160; <span class="keywordflow">return</span> Simple4dSoftmaxTestImpl&lt;armnn::DataType::QSymmS16&gt;(workloadFactory, memoryManager, beta,</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>&#160; data.inputShape, data.outputData, data.inputData);</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>&#160;}</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>&#160;</div><div class="line"><a name="l00690"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#a9233e1ce61a19fee2d31587c92643c47"> 690</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;float,2&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a19eddd116e2b25fe1e3f5dabe7811881">CompareSoftmaxTest</a>(</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; refWorkloadFactory,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>&#160;{</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>&#160; <span class="keywordflow">return</span> CompareSoftmaxTestImpl&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>&#160; workloadFactory, memoryManager, refWorkloadFactory, beta);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>&#160;}</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>&#160;</div><div class="line"><a name="l00700"></a><span class="lineno"><a class="line" href="_softmax_test_impl_8hpp.xhtml#acb285bcbab3614dd25d3536664fb27a7"> 700</a></span>&#160;<a class="code" href="struct_layer_test_result.xhtml">LayerTestResult&lt;uint8_t,2&gt;</a> <a class="code" href="_softmax_test_impl_8cpp.xhtml#a6e109a180e36e8184f3e6193110ce709">CompareSoftmaxUint8Test</a>(</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&amp; refWorkloadFactory,</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>&#160; <span class="keywordtype">float</span> beta)</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>&#160;{</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>&#160; <span class="keywordflow">return</span> CompareSoftmaxTestImpl&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>&#160; workloadFactory, memoryManager, refWorkloadFactory, beta);</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>&#160;}</div><div class="ttc" id="_tensor_copy_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_copy_utils_8hpp.xhtml">TensorCopyUtils.hpp</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a55ce19436218d7abbbc53b5f66c2b7fe"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a55ce19436218d7abbbc53b5f66c2b7fe">SimpleSoftmaxUint16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 2 &gt; SimpleSoftmaxUint16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00661">SoftmaxTestImpl.cpp:661</a></div></div>
+<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a214c3636fdf0ea5bac8edb42d0e6c7f0"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">armnn::SoftmaxDescriptor::m_Axis</a></div><div class="ttdeci">int m_Axis</div><div class="ttdoc">Scalar, defaulted to the last index (-1), specifying the dimension the activation will be performed o...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00138">Descriptors.hpp:138</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
+<div class="ttc" id="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div>
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+<div class="ttc" id="_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a2363ddc02a7035b224ab4bbc548c7e35"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a2363ddc02a7035b224ab4bbc548c7e35">Simple3dSoftmaxUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 3 &gt; Simple3dSoftmaxUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00607">SoftmaxTestImpl.cpp:607</a></div></div>
+<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::SoftmaxDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Exponentiation value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00136">Descriptors.hpp:136</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_abde790042baa52b75247c30559840cbf"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#abde790042baa52b75247c30559840cbf">Simple3dSoftmaxUint16Test</a></div><div class="ttdeci">LayerTestResult&lt; int16_t, 3 &gt; Simple3dSoftmaxUint16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00669">SoftmaxTestImpl.cpp:669</a></div></div>
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+<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
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+<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
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+<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a1f0c412bd42fe4ef6b408d463a7a438f"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a1f0c412bd42fe4ef6b408d463a7a438f">Simple3dAxisSoftmaxTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 3 &gt; Simple3dAxisSoftmaxTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta, int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00331">SoftmaxTestImpl.cpp:331</a></div></div>
+<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a493bef3f5fc0d657b0a5fe29e58dcbdf"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a493bef3f5fc0d657b0a5fe29e58dcbdf">SimpleAxisSoftmaxTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 2 &gt; SimpleAxisSoftmaxTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta, int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00312">SoftmaxTestImpl.cpp:312</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8hpp_xhtml"><div class="ttname"><a href="_softmax_test_impl_8hpp.xhtml">SoftmaxTestImpl.hpp</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a8a843d44d2e81df87e414df3b3e688de"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a8a843d44d2e81df87e414df3b3e688de">armnn::IWorkloadFactory::CreateSoftmax</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateSoftmax(const SoftmaxQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01400">WorkloadFactory.cpp:1400</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a49081ef56cfc5fafad212dfbce4f259b"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a49081ef56cfc5fafad212dfbce4f259b">SimpleSoftmaxTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 2 &gt; SimpleSoftmaxTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00304">SoftmaxTestImpl.cpp:304</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#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.xhtml#l00275">Tensor.cpp:275</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a1dc1b7112df501f911a501ad40589215"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a1dc1b7112df501f911a501ad40589215">Simple4dAxisSoftmaxTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; Simple4dAxisSoftmaxTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta, int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00435">SoftmaxTestImpl.cpp:435</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a0d366093ec6ca27079466c811151665c"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a0d366093ec6ca27079466c811151665c">Simple4dSoftmaxTest</a></div><div class="ttdeci">LayerTestResult&lt; float, 4 &gt; Simple4dSoftmaxTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00425">SoftmaxTestImpl.cpp:425</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a6e109a180e36e8184f3e6193110ce709"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a6e109a180e36e8184f3e6193110ce709">CompareSoftmaxUint8Test</a></div><div class="ttdeci">LayerTestResult&lt; uint8_t, 2 &gt; CompareSoftmaxUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::IWorkloadFactory &amp;refWorkloadFactory, float beta)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00700">SoftmaxTestImpl.cpp:700</a></div></div>
+<div class="ttc" id="_softmax_test_impl_8cpp_xhtml_a28b9861c52ee758d11db282794b21306"><div class="ttname"><a href="_softmax_test_impl_8cpp.xhtml#a28b9861c52ee758d11db282794b21306">SimpleSoftmaxFloat16Test</a></div><div class="ttdeci">LayerTestResult&lt; armnn::Half, 2 &gt; SimpleSoftmaxFloat16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, float beta)</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_test_impl_8cpp_source.xhtml#l00633">SoftmaxTestImpl.cpp:633</a></div></div>
+<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
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