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<a href="backends_2reference_2workloads_2_mean_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> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_mean_8hpp.xhtml">Mean.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_workload_data_8hpp.xhtml">backendsCommon/WorkloadData.hpp</a>></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <cstddef></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <functional></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <limits></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> {</div><div class="line"><a name="l00018"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a869f740e9c2fcb8642350c6e3d0b3742"> 18</a></span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.xhtml#a869f740e9c2fcb8642350c6e3d0b3742">NextIndex</a>(<span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& dims, std::vector<unsigned int>& current)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> carry = 1;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = numDims; idx-- > 0; )</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> current_val = current[idx] + carry;</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordflow">if</span> (dims[idx] == current_val)</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  current[idx] = 0;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  }</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  {</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  current[idx] = current_val;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  carry = 0;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  }</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  }</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">return</span> (carry == 0);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> }</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ae86f1ca23eaa764da9e589cc8e39a969"> 39</a></span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearmnn.xhtml#ae86f1ca23eaa764da9e589cc8e39a969">ReducedOutputOffset</a>(<span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims,</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& dims,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  std::vector<unsigned int>& index,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numAxis,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> std::vector<unsigned int>& axis)</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> offset = 0;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx < numDims; ++idx)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">bool</span> isAxis = <span class="keyword">false</span>;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">if</span> (!axis.empty())</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> axisIdx = 0; axisIdx < numAxis; ++axisIdx)</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">if</span> (idx == axis[axisIdx])</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  isAxis = <span class="keyword">true</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">if</span> (!isAxis)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  offset = offset * dims[idx] + index[idx];</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  }</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">return</span> offset;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div><div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a165ae372a7f67cad64ef3395d30122ce"> 71</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a165ae372a7f67cad64ef3395d30122ce">Mean</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> std::vector<unsigned int>& axis,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& input,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="classarmnn_1_1_encoder.xhtml">Encoder<float></a>& output)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNumDims = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNumDims = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> outputDims = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputDims = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// Initialise output data.</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputs = 1;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx < outputNumDims; ++idx)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  numOutputs *= outputDims[idx];</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  std::vector<float> tempSum(numOutputs);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx < numOutputs; ++idx)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  output[idx];</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  output.<a class="code" href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">Set</a>(0.0f);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  tempSum[idx] = 0.0f;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// Initialise temp index.</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  std::vector<unsigned int> tempIndex(inputNumDims);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx < inputNumDims; ++idx)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  tempIndex[idx] = 0;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  std::vector<unsigned int> resolvedAxis = axis;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">if</span> (resolvedAxis.empty())</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  {</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx < inputNumDims; ++idx)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  resolvedAxis.push_back(idx);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">auto</span> numResolvedAxis = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(resolvedAxis.size());</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// Iterates through input_data and sum up the reduced axis.</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordflow">for</span> (<span class="keywordtype">bool</span> hasNext = <span class="keyword">true</span>; hasNext; hasNext = <a class="code" href="namespacearmnn.xhtml#a869f740e9c2fcb8642350c6e3d0b3742">NextIndex</a>(inputNumDims, inputDims, tempIndex))</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputOffset = <a class="code" href="namespacearmnn.xhtml#ae86f1ca23eaa764da9e589cc8e39a969">ReducedOutputOffset</a>(inputNumDims, inputDims, tempIndex, 0, {});</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputOffset = <a class="code" href="namespacearmnn.xhtml#ae86f1ca23eaa764da9e589cc8e39a969">ReducedOutputOffset</a>(inputNumDims, inputDims, tempIndex,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  numResolvedAxis, resolvedAxis);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  input[inputOffset];</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  tempSum[outputOffset] += input.<a class="code" href="classarmnn_1_1_decoder.xhtml#ac729108381e2340bea12877971713ecb">Get</a>();</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// Takes average by num of elements added to get mean.</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordtype">size_t</span> numElementsInAxis = 1;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx < numResolvedAxis; ++idx)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> current = inputDims[resolvedAxis[idx]];</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  BOOST_ASSERT(boost::numeric_cast<float>(current) <</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  (std::numeric_limits<float>::max() / boost::numeric_cast<float>(numElementsInAxis)));</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  numElementsInAxis *= current;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">if</span> (numElementsInAxis > 0) {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx < numOutputs; ++idx)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  {</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  output[idx];</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  output.<a class="code" href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">Set</a>(tempSum[idx] / boost::numeric_cast<float>(numElementsInAxis));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  }</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> }</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> } <span class="comment">//namespace armnn</span></div><div class="ttc" id="namespacearmnn_xhtml_a869f740e9c2fcb8642350c6e3d0b3742"><div class="ttname"><a href="namespacearmnn.xhtml#a869f740e9c2fcb8642350c6e3d0b3742">armnn::NextIndex</a></div><div class="ttdeci">bool NextIndex(const unsigned int numDims, const armnn::TensorShape &dims, std::vector< unsigned int > &current)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_mean_8cpp_source.xhtml#l00018">Mean.cpp:18</a></div></div>
<div class="ttc" id="_workload_data_8hpp_xhtml"><div class="ttname"><a href="_workload_data_8hpp.xhtml">WorkloadData.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae86f1ca23eaa764da9e589cc8e39a969"><div class="ttname"><a href="namespacearmnn.xhtml#ae86f1ca23eaa764da9e589cc8e39a969">armnn::ReducedOutputOffset</a></div><div class="ttdeci">unsigned int ReducedOutputOffset(const unsigned int numDims, const armnn::TensorShape &dims, std::vector< unsigned int > &index, const unsigned int numAxis, const std::vector< unsigned int > &axis)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_mean_8cpp_source.xhtml#l00039">Mean.cpp:39</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="classarmnn_1_1_encoder_xhtml_ae3b62b846a9c239f332830b9e36030eb"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">armnn::Encoder::Set</a></div><div class="ttdeci">virtual void Set(IType right)=0</div></div>
<div class="ttc" id="classarmnn_1_1_encoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml">armnn::Encoder< float ></a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_xhtml_ac729108381e2340bea12877971713ecb"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml#ac729108381e2340bea12877971713ecb">armnn::Decoder::Get</a></div><div class="ttdeci">virtual IType Get() const =0</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div>
<div class="ttc" id="_mean_8hpp_xhtml"><div class="ttname"><a href="_mean_8hpp.xhtml">Mean.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a165ae372a7f67cad64ef3395d30122ce"><div class="ttname"><a href="namespacearmnn.xhtml#a165ae372a7f67cad64ef3395d30122ce">armnn::Mean</a></div><div class="ttdeci">void Mean(const armnn::TensorInfo &inputInfo, const armnn::TensorInfo &outputInfo, const std::vector< unsigned int > &axis, Decoder< float > &input, Encoder< float > &output)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_mean_8cpp_source.xhtml#l00071">Mean.cpp:71</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00092">Tensor.hpp:92</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml">armnn::Decoder< float ></a></div></div>
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