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<a href="backends_2reference_2workloads_2_pad_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="_pad_8hpp.xhtml">Pad.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> <span class="preprocessor">#include "<a class="code" href="_tensor_buffer_array_view_8hpp.xhtml">TensorBufferArrayView.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "<a class="code" href="_encoders_8hpp.xhtml">Encoders.hpp</a>"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <cstddef></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <functional></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <limits></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <cassert></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></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> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00022"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a28e115f5d28500324b53fae9e6c00b77"> 22</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a28e115f5d28500324b53fae9e6c00b77">Pad</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  std::vector<std::pair<unsigned int, unsigned int>> m_padList,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> T* inputData,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  T* outData,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> padValue)</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputElements = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputDimensions = inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</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="preprocessor"> #ifndef NDEBUG</span></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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputDimensions = outputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  assert(numInputDimensions == numOutputDimensions);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor"> #endif</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatches = 0;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 0;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 0;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 0;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 0;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 0;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 0;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  T convertedPadValue = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(padValue);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numOutputElements; ++i)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  outData[i] = convertedPadValue;</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>  <span class="keywordflow">switch</span>(numInputDimensions) {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  inputWidth = inputShape[0];</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">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < inputWidth ; w++)</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>  outData[w+std::get<0>(m_padList[0])] = inputData[w];</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> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">case</span> 2 :</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  inputHeight = inputShape[0];</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  inputWidth = inputShape[1];</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  outputHeight = outputShape[0];</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  outputWidth = outputShape[1];</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> h = 0; h < inputHeight; h++)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < inputWidth ; w++)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  outData[(h+std::get<0>(m_padList[0]))*outputWidth</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  + (w+std::get<0>(m_padList[1]))] = inputData[h * inputWidth + w];</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  }</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</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>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> 3 :</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  inputChannels = inputShape[0];</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  inputHeight = inputShape[1];</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  inputWidth = inputShape[2];</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  outputChannels = outputShape[0];</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  outputHeight = outputShape[1];</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  outputWidth = outputShape[2];</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> c = 0; c < inputChannels; c++)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  {</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> h = 0; h < inputHeight; h++)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < inputWidth ; w++)</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>  outData[(c+std::get<0>(m_padList[0]))*outputHeight*outputWidth</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  + (h+std::get<0>(m_padList[1]))*outputWidth</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  + (w+std::get<0>(m_padList[2]))] = inputData[c * inputHeight * inputWidth</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  + h * inputWidth</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  + w];</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>  }</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="keywordflow">break</span>;</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="keywordflow">case</span> 4 :</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  inputBatches = inputShape[0];</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  inputChannels = inputShape[1];</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  inputHeight = inputShape[2];</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  inputWidth = inputShape[3];</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  outputChannels = outputShape[1];</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  outputHeight = outputShape[2];</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  outputWidth = outputShape[3];</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> b = 0; b < inputBatches; b++)</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {</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> c = 0; c < inputChannels; c++)</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> h = 0; h < inputHeight; h++)</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < inputWidth ; w++)</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  outData[(b+std::get<0>(m_padList[0])) * outputChannels * outputHeight * outputWidth</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  + (c+std::get<0>(m_padList[1])) * outputHeight * outputWidth</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  + (h+std::get<0>(m_padList[2])) * outputWidth</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  + (w+std::get<0>(m_padList[3]))] = inputData[b * inputChannels * inputHeight</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  * inputWidth</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  + c * inputHeight * inputWidth</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  + h * inputWidth</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  + w];</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>  }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  }</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  default :</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="keyword">template</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a37fe5e5b5f650430dc0e71d69977bebd">Pad<BFloat16></a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  std::vector<std::pair<unsigned int, unsigned int>> m_PadList,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_b_float16.xhtml">BFloat16</a>* inputData,</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classarmnn_1_1_b_float16.xhtml">BFloat16</a>* outData,</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> padValue);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="keyword">template</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a09fc687543b371ddab280203dc989bd9">Pad<float></a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  std::vector<std::pair<unsigned int, unsigned int>> m_PadList,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* inputData,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordtype">float</span>* outData,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> padValue);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="keyword">template</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a1b165f49b29968defb57e2d9b8628b9f">Pad<Half></a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  std::vector<std::pair<unsigned int, unsigned int>> m_PadList,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>* inputData,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">Half</a>* outData,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> padValue);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="keyword">template</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a7e27cbebab8cde65c84d7a00efa025cd">Pad<uint8_t></a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  std::vector<std::pair<unsigned int, unsigned int>> m_PadList,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">const</span> uint8_t* inputData,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  uint8_t* outData,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> padValue);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="keyword">template</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a68b05cecb5ebbbc3b8d1fd94a66df4af">Pad<int16_t></a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  std::vector<std::pair<unsigned int, unsigned int>> m_PadList,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">const</span> int16_t* inputData,</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  int16_t* outData,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> padValue);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> } <span class="comment">//namespace armnn</span></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="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_b_float16_xhtml"><div class="ttname"><a href="classarmnn_1_1_b_float16.xhtml">armnn::BFloat16</a></div><div class="ttdef"><b>Definition:</b> <a href="_b_float16_8hpp_source.xhtml#l00014">BFloat16.hpp:14</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="_tensor_buffer_array_view_8hpp_xhtml"><div class="ttname"><a href="_tensor_buffer_array_view_8hpp.xhtml">TensorBufferArrayView.hpp</a></div></div>
<div class="ttc" id="_encoders_8hpp_xhtml"><div class="ttname"><a href="_encoders_8hpp.xhtml">Encoders.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a28e115f5d28500324b53fae9e6c00b77"><div class="ttname"><a href="namespacearmnn.xhtml#a28e115f5d28500324b53fae9e6c00b77">armnn::Pad</a></div><div class="ttdeci">void Pad(const TensorInfo &inputInfo, const TensorInfo &outputInfo, std::vector< std::pair< unsigned int, unsigned int >> m_padList, const T *inputData, T *outData, const float padValue)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_pad_8cpp_source.xhtml#l00022">Pad.cpp:22</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a37fe5e5b5f650430dc0e71d69977bebd"><div class="ttname"><a href="namespacearmnn.xhtml#a37fe5e5b5f650430dc0e71d69977bebd">armnn::Pad< BFloat16 ></a></div><div class="ttdeci">template void Pad< BFloat16 >(const TensorInfo &inputInfo, const TensorInfo &outputInfo, std::vector< std::pair< unsigned int, unsigned int >> m_PadList, const BFloat16 *inputData, BFloat16 *outData, const float padValue)</div></div>
<div class="ttc" id="_pad_8hpp_xhtml"><div class="ttname"><a href="_pad_8hpp.xhtml">Pad.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a68b05cecb5ebbbc3b8d1fd94a66df4af"><div class="ttname"><a href="namespacearmnn.xhtml#a68b05cecb5ebbbc3b8d1fd94a66df4af">armnn::Pad< int16_t ></a></div><div class="ttdeci">template void Pad< int16_t >(const TensorInfo &inputInfo, const TensorInfo &outputInfo, std::vector< std::pair< unsigned int, unsigned int >> m_PadList, const int16_t *inputData, int16_t *outData, const float padValue)</div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00043">Tensor.hpp:43</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a1b165f49b29968defb57e2d9b8628b9f"><div class="ttname"><a href="namespacearmnn.xhtml#a1b165f49b29968defb57e2d9b8628b9f">armnn::Pad< Half ></a></div><div class="ttdeci">template void Pad< Half >(const TensorInfo &inputInfo, const TensorInfo &outputInfo, std::vector< std::pair< unsigned int, unsigned int >> m_PadList, const Half *inputData, Half *outData, const float padValue)</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a09fc687543b371ddab280203dc989bd9"><div class="ttname"><a href="namespacearmnn.xhtml#a09fc687543b371ddab280203dc989bd9">armnn::Pad< float ></a></div><div class="ttdeci">template void Pad< float >(const TensorInfo &inputInfo, const TensorInfo &outputInfo, std::vector< std::pair< unsigned int, unsigned int >> m_PadList, const float *inputData, float *outData, const float padValue)</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a0f38fa92b2468d5378258a2b074c1a31"><div class="ttname"><a href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">armnn::Half</a></div><div class="ttdeci">half_float::half Half</div><div class="ttdef"><b>Definition:</b> <a href="_half_8hpp_source.xhtml#l00016">Half.hpp:16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a7e27cbebab8cde65c84d7a00efa025cd"><div class="ttname"><a href="namespacearmnn.xhtml#a7e27cbebab8cde65c84d7a00efa025cd">armnn::Pad< uint8_t ></a></div><div class="ttdeci">template void Pad< uint8_t >(const TensorInfo &inputInfo, const TensorInfo &outputInfo, std::vector< std::pair< unsigned int, unsigned int >> m_PadList, const uint8_t *inputData, uint8_t *outData, const float padValue)</div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00093">Tensor.hpp:93</a></div></div>
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