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<a href="backends_2reference_2workloads_2_mean_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_mean_8hpp.html">Mean.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_data_8hpp.html">backendsCommon/WorkloadData.hpp</a>&gt;</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &lt;boost/numeric/conversion/cast.hpp&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;<span class="preprocessor">#include &lt;cmath&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="preprocessor">#include &lt;cstddef&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="preprocessor">#include &lt;functional&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="preprocessor">#include &lt;limits&gt;</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;{</div><div class="line"><a name="l00018"></a><span class="lineno"><a class="line" href="namespacearmnn.html#a869f740e9c2fcb8642350c6e3d0b3742"> 18</a></span>&#160;<span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.html#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.html">armnn::TensorShape</a>&amp; dims, std::vector&lt;unsigned int&gt;&amp; current)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;{</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> carry = 1;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = numDims; idx-- &gt; 0; )</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="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>&#160; <span class="keywordflow">if</span> (dims[idx] == current_val)</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160; {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; current[idx] = 0;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; }</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; <span class="keywordflow">else</span></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; current[idx] = current_val;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; carry = 0;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; }</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; }</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="keywordflow">return</span> (carry == 0);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;}</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"><a class="line" href="namespacearmnn.html#ae86f1ca23eaa764da9e589cc8e39a969"> 39</a></span>&#160;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearmnn.html#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>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>&amp; dims,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; std::vector&lt;unsigned int&gt;&amp; index,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <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>&#160; <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; axis)</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> offset = 0;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx &lt; numDims; ++idx)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="keywordtype">bool</span> isAxis = <span class="keyword">false</span>;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="keywordflow">if</span> (!axis.empty())</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> axisIdx = 0; axisIdx &lt; numAxis; ++axisIdx)</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; <span class="keywordflow">if</span> (idx == axis[axisIdx])</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; isAxis = <span class="keyword">true</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="keywordflow">if</span> (!isAxis)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; offset = offset * dims[idx] + index[idx];</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; }</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keywordflow">return</span> offset;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;}</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;{</div><div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="namespacearmnn.html#a165ae372a7f67cad64ef3395d30122ce"> 71</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#a165ae372a7f67cad64ef3395d30122ce">Mean</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; inputInfo,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; outputInfo,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; axis,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <a class="code" href="classarmnn_1_1_decoder.html">Decoder&lt;float&gt;</a>&amp; input,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <a class="code" href="classarmnn_1_1_encoder.html">Encoder&lt;float&gt;</a>&amp; output)</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;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNumDims = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNumDims = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</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; <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a> outputDims = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a> inputDims = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="comment">// Initialise output data.</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <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>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx &lt; outputNumDims; ++idx)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; numOutputs *= outputDims[idx];</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; std::vector&lt;float&gt; tempSum(numOutputs);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx &lt; numOutputs; ++idx)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; output[idx];</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; output.<a class="code" href="classarmnn_1_1_encoder.html#ae3b62b846a9c239f332830b9e36030eb">Set</a>(0.0f);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; tempSum[idx] = 0.0f;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; <span class="comment">// Initialise temp index.</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; std::vector&lt;unsigned int&gt; tempIndex(inputNumDims);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx &lt; inputNumDims; ++idx)</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; tempIndex[idx] = 0;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; std::vector&lt;unsigned int&gt; resolvedAxis = axis;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="keywordflow">if</span> (resolvedAxis.empty())</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; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx &lt; inputNumDims; ++idx)</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; resolvedAxis.push_back(idx);</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; }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keyword">auto</span> numResolvedAxis = boost::numeric_cast&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(resolvedAxis.size());</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="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>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">bool</span> hasNext = <span class="keyword">true</span>; hasNext; hasNext = <a class="code" href="namespacearmnn.html#a869f740e9c2fcb8642350c6e3d0b3742">NextIndex</a>(inputNumDims, inputDims, tempIndex))</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputOffset = <a class="code" href="namespacearmnn.html#ae86f1ca23eaa764da9e589cc8e39a969">ReducedOutputOffset</a>(inputNumDims, inputDims, tempIndex, 0, {});</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputOffset = <a class="code" href="namespacearmnn.html#ae86f1ca23eaa764da9e589cc8e39a969">ReducedOutputOffset</a>(inputNumDims, inputDims, tempIndex,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; numResolvedAxis, resolvedAxis);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; input[inputOffset];</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; tempSum[outputOffset] += input.<a class="code" href="classarmnn_1_1_decoder.html#ac729108381e2340bea12877971713ecb">Get</a>();</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;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <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>&#160; <span class="keywordtype">size_t</span> numElementsInAxis = 1;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx &lt; numResolvedAxis; ++idx)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <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>&#160; BOOST_ASSERT(boost::numeric_cast&lt;float&gt;(current) &lt;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; (std::numeric_limits&lt;float&gt;::max() / boost::numeric_cast&lt;float&gt;(numElementsInAxis)));</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; numElementsInAxis *= current;</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="keywordflow">if</span> (numElementsInAxis &gt; 0) {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0; idx &lt; numOutputs; ++idx)</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; output[idx];</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; output.<a class="code" href="classarmnn_1_1_encoder.html#ae3b62b846a9c239f332830b9e36030eb">Set</a>(tempSum[idx] / boost::numeric_cast&lt;float&gt;(numElementsInAxis));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; }</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;} <span class="comment">//namespace armnn</span></div><div class="ttc" id="classarmnn_1_1_tensor_info_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00092">Tensor.hpp:92</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_html"><div class="ttname"><a href="classarmnn_1_1_decoder.html">armnn::Decoder&lt; float &gt;</a></div></div>
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<div class="ttc" id="classarmnn_1_1_encoder_html"><div class="ttname"><a href="classarmnn_1_1_encoder.html">armnn::Encoder&lt; float &gt;</a></div></div>
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<div class="ttc" id="_workload_data_8hpp_html"><div class="ttname"><a href="_workload_data_8hpp.html">WorkloadData.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_helper_8hpp_source.html#l00011">BackendHelper.hpp:11</a></div></div>
<div class="ttc" id="_mean_8hpp_html"><div class="ttname"><a href="_mean_8hpp.html">Mean.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_html"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="namespacearmnn_html_ae86f1ca23eaa764da9e589cc8e39a969"><div class="ttname"><a href="namespacearmnn.html#ae86f1ca23eaa764da9e589cc8e39a969">armnn::ReducedOutputOffset</a></div><div class="ttdeci">unsigned int ReducedOutputOffset(const unsigned int numDims, const armnn::TensorShape &amp;dims, std::vector&lt; unsigned int &gt; &amp;index, const unsigned int numAxis, const std::vector&lt; unsigned int &gt; &amp;axis)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_mean_8cpp_source.html#l00039">Mean.cpp:39</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_html_ac729108381e2340bea12877971713ecb"><div class="ttname"><a href="classarmnn_1_1_decoder.html#ac729108381e2340bea12877971713ecb">armnn::Decoder::Get</a></div><div class="ttdeci">virtual IType Get() const =0</div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00088">Tensor.hpp:88</a></div></div>
<div class="ttc" id="classarmnn_1_1_encoder_html_ae3b62b846a9c239f332830b9e36030eb"><div class="ttname"><a href="classarmnn_1_1_encoder.html#ae3b62b846a9c239f332830b9e36030eb">armnn::Encoder::Set</a></div><div class="ttdeci">virtual void Set(IType right)=0</div></div>
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