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<div class="title">WorkloadUtils.hpp</div> </div>
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<a href="_workload_utils_8hpp.html">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, 2023 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">#pragma once</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span>  </div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_i_tensor_handle_8hpp.html">armnn/backends/ITensorHandle.hpp</a>></span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_tensor_handle_8hpp.html">armnn/backends/TensorHandle.hpp</a>></span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_tensor_8hpp.html">armnn/Tensor.hpp</a>></span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_polymorphic_downcast_8hpp.html">armnn/utility/PolymorphicDowncast.hpp</a>></span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.html">armnnUtils/Permute.hpp</a>></span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  </div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_half_8hpp.html">Half.hpp</a>></span></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_profiling_8hpp.html">Profiling.hpp</a>></span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</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.html">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> <span class="keyword">namespace</span></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>  </div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ArrayType, <span class="keyword">typename</span> Arg></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keywordtype">void</span> AssignValues(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>& idx, <span class="keyword">const</span> ArrayType& array, Arg& arg)</div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> {</div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordflow">if</span> (idx >= num)</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  {</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</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>  arg = array[(num - 1) - idx];</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  idx++;</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>  </div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> ArrayType, <span class="keyword">typename</span>... Args></div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keywordtype">void</span> AssignValues(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx, <span class="keyword">const</span> ArrayType& array, T& assignee, Args&... args)</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>  AssignValues(num, idx, array, assignee);</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  </div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  AssignValues(num, idx, array, args...);</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</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="comment">// anonymous namespace</span></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="keyword">template</span> <<span class="keyword">typename</span> CopyFunc></div>
<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="namespacearmnn.html#a92c91193007aa49f4732d6dba5397f8d"> 46</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#a92c91193007aa49f4732d6dba5397f8d">CopyTensorContentsGeneric</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle.html">ITensorHandle</a>* srcTensor, <a class="code" href="classarmnn_1_1_i_tensor_handle.html">ITensorHandle</a>* dstTensor, CopyFunc copy)</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="comment">// For ease of understanding, names are assigned to the dimensions</span></div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// of the tensor as if NHWC, however this routine works with any 5D tensor</span></div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  static_assert(<a class="code" href="namespacearmnn.html#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a> == 5, <span class="stringliteral">"Please update CopyTensorContents"</span>);</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>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> srcStrides = srcTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a30c3e09ce55369b66469443a4ca5ef03">GetStrides</a>();</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& srcShape = srcTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#affd5aae75cad90f472f96cfd25a13f29">GetShape</a>();</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <span class="keyword">auto</span> srcSize = srcTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a30c3e09ce55369b66469443a4ca5ef03">GetStrides</a>()[0] * srcShape[0];</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> dstStrides = dstTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a30c3e09ce55369b66469443a4ca5ef03">GetStrides</a>();</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& dstShape = dstTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#affd5aae75cad90f472f96cfd25a13f29">GetShape</a>();</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">const</span> <span class="keyword">auto</span> dstSize = dstTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a30c3e09ce55369b66469443a4ca5ef03">GetStrides</a>()[0] * dstShape[0];</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="keywordtype">size_t</span> srcDepth = 1;</div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">size_t</span> srcBatches = 1;</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordtype">size_t</span> srcHeight = 1;</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">size_t</span> srcWidth = 1;</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordtype">size_t</span> srcChannels = 1;</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  AssignValues(srcShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(),</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  0,</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  srcShape,</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  srcChannels,</div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  srcWidth,</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  srcHeight,</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  srcBatches,</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  srcDepth);</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  </div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordtype">size_t</span> srcDepthStride = 0;</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">size_t</span> srcBatchStride = 0;</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordtype">size_t</span> srcHeightStride = 0;</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordtype">size_t</span> srcWidthStride = 0;</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordtype">size_t</span> srcChannelStride = 0;</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  AssignValues(srcStrides.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(),</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  0,</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  srcStrides,</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  srcChannelStride,</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  srcWidthStride,</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  srcHeightStride,</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  srcBatchStride,</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  srcDepthStride);</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>  <span class="keywordtype">size_t</span> dstDepth = 1;</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordtype">size_t</span> dstBatches = 1;</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordtype">size_t</span> dstHeight = 1;</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordtype">size_t</span> dstWidth = 1;</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordtype">size_t</span> dstChannels = 1;</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  AssignValues(dstShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(),</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  0,</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  dstShape,</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  dstChannels,</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  dstWidth,</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  dstHeight,</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  dstBatches,</div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  dstDepth);</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="keywordtype">size_t</span> dstDepthStride = 0;</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordtype">size_t</span> dstBatchStride = 0;</div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordtype">size_t</span> dstHeightStride = 0;</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordtype">size_t</span> dstWidthStride = 0;</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordtype">size_t</span> dstChannelStride = 0;</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  AssignValues(dstStrides.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(),</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  0,</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  dstStrides,</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  dstChannelStride,</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  dstWidthStride,</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  dstHeightStride,</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  dstBatchStride,</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  dstDepthStride);</div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  </div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>* srcDataStart;</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>* dstDataStart;</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>  <a class="code" href="_profiling_8hpp.html#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.html#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"Synchronize buffers"</span>);</div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  srcDataStart = <span class="keyword">static_cast<</span><span class="keyword">const </span>uint8_t*<span class="keyword">></span>(srcTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a9afbc055a017adf1bc38ee137bca6e90">Map</a>());</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  dstDataStart = <span class="keyword">static_cast<</span>uint8_t*<span class="keyword">></span>(dstTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a9afbc055a017adf1bc38ee137bca6e90">Map</a>());</div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">if</span> (srcDataStart == <span class="keyword">nullptr</span>)</div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  {</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_validation_exception.html">MemoryValidationException</a>(<span class="stringliteral">"The source tensor is null."</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="keywordflow">if</span> (dstDataStart == <span class="keyword">nullptr</span>)</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">throw</span> <a class="code" href="classarmnn_1_1_memory_validation_exception.html">MemoryValidationException</a>(<span class="stringliteral">"The destination tensor is null."</span>);</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>  </div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">size_t</span> copyLength = std::min(srcChannels * srcChannelStride, dstChannels * dstChannelStride);</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordtype">size_t</span> copyWidth = std::min(srcWidth, dstWidth);</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordtype">size_t</span> copyHeight = std::min(srcHeight, dstHeight);</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordtype">size_t</span> copyBatches = std::min(srcBatches, dstBatches);</div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordtype">size_t</span> copyDepth = std::min(srcDepth, dstDepth);</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  </div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Coalesce inner dimensions where possible</span></div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="comment">// to reduce overheard calling copy() and to</span></div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// allow for memory bandwidth optimisations</span></div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">if</span> (copyLength == srcWidthStride &&</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  copyLength == dstWidthStride)</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">// There is no special padding between rows,</span></div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// and sizes are compatible, so copy whole rows</span></div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  copyLength *= copyWidth;</div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  copyWidth = 1;</div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  </div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">if</span> (copyLength == srcHeightStride &&</div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  copyLength == dstHeightStride)</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="comment">// There is no special padding between batches</span></div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// and sizes are compatible so copy whole batches</span></div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  copyLength *= copyHeight;</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  copyHeight = 1;</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  }</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  </div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>* srcData = srcDataStart;</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>* dstData = dstDataStart;</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 0; d < copyDepth; ++d)</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">auto</span> srcPtrDepth = srcData;</div>
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">auto</span> dstPtrDepth = dstData;</div>
<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> b = 0; b < copyBatches; ++b)</div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  {</div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keyword">auto</span> srcPtrBatch = srcData;</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keyword">auto</span> dstPtrBatch = dstData;</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> h = 0; h < copyHeight; ++h)</div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  {</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">auto</span> srcPtrChannel = srcData;</div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">auto</span> dstPtrChannel = dstData;</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < copyWidth; ++w)</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  {</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="comment">// Sanity check the memory area we've been asked to copy from and to.</span></div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">if</span> (copyLength > srcSize)</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  {</div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_validation_exception.html">MemoryValidationException</a>(</div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="stringliteral">"The source tensor size does not match the size of the allocated tensor."</span>);</div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  }</div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">if</span> (copyLength > dstSize)</div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  {</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_validation_exception.html">MemoryValidationException</a>(</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="stringliteral">"The destination tensor size will overrun the destination tensor."</span>);</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  }</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  copy(dstData, srcData, copyLength);</div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  dstData += dstWidthStride;</div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  srcData += srcWidthStride;</div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  }</div>
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  dstData += (<span class="keyword">static_cast<</span><span class="keywordtype">long</span><span class="keyword">></span>(dstHeightStride) - (dstData - dstPtrChannel));</div>
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  srcData += (<span class="keyword">static_cast<</span><span class="keywordtype">long</span><span class="keyword">></span>(srcHeightStride) - (srcData - srcPtrChannel));</div>
<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div>
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  dstData += (<span class="keyword">static_cast<</span><span class="keywordtype">long</span><span class="keyword">></span>(dstBatchStride) - (dstData - dstPtrBatch));</div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  srcData += (<span class="keyword">static_cast<</span><span class="keywordtype">long</span><span class="keyword">></span>(srcBatchStride) - (srcData - srcPtrBatch));</div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  dstData += (<span class="keyword">static_cast<</span><span class="keywordtype">long</span><span class="keyword">></span>(dstDepthStride) - (dstData - dstPtrDepth));</div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  srcData += (<span class="keyword">static_cast<</span><span class="keywordtype">long</span><span class="keyword">></span>(srcDepthStride) - (srcData - srcPtrDepth));</div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  }</div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  </div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  srcTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a563609828050f1b3a7868c23f3365923">Unmap</a>();</div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  dstTensor-><a class="code" href="classarmnn_1_1_i_tensor_handle.html#a563609828050f1b3a7868c23f3365923">Unmap</a>();</div>
<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> }</div>
<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  </div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SrcTensorHandleType, <span class="keyword">typename</span> DstTensorHandleType, <span class="keyword">typename</span> DescriptorType></div>
<div class="line"><a name="l00204"></a><span class="lineno"><a class="line" href="namespacearmnn.html#a844d7fa4dde82e1c6b0606e1c68890bb"> 204</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#a844d7fa4dde82e1c6b0606e1c68890bb">GatherTensorHandlePairs</a>(<span class="keyword">const</span> DescriptorType& descriptor,</div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  std::vector<std::pair<SrcTensorHandleType*, DstTensorHandleType*>>& tensorHandlePairs)</div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span> {</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(descriptor.m_Inputs.size());</div>
<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  tensorHandlePairs.reserve(numInputs);</div>
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  </div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numInputs; ++i)</div>
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  {</div>
<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  SrcTensorHandleType* <span class="keyword">const</span> srcTensorHandle =</div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  PolymorphicDowncast<SrcTensorHandleType*>(descriptor.m_Inputs[i]);</div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  DstTensorHandleType* <span class="keyword">const</span> dstTensorHandle =</div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  PolymorphicDowncast<DstTensorHandleType*>(descriptor.m_Outputs[i]);</div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  </div>
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  tensorHandlePairs.emplace_back(srcTensorHandle, dstTensorHandle);</div>
<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  }</div>
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span> }</div>
<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  </div>
<div class="line"><a name="l00221"></a><span class="lineno"> 221</span> int32_t <a class="code" href="namespacearmnn.html#ad69ffa576a596b9eb20ab6a41420c541">ConvertMaskToACLFormat</a>(int32_t mask, int32_t numDim);</div>
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  </div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <a class="code" href="classarmnn_1_1_const_tensor.html">armnn::ConstTensor</a> <a class="code" href="namespacearmnn.html#aafe6180ef80d9f334f3a3ba9cc0db35d">PermuteTensor</a>(<span class="keyword">const</span> ConstTensorHandle* tensor,</div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keyword">const</span> PermutationVector& permutationVector,</div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordtype">void</span>* permuteBuffer);</div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  </div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#a3170fdd696155a247ecd81d445c0e2e1">ReshapeWeightsForAcl</a>(TensorInfo& weightInfo, <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout);</div>
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  </div>
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> TensorInfo <a class="code" href="namespacearmnn.html#a1e8288eac7e909fdb58b6113d816763a">ConvertWeightTensorInfoFromArmnnToAcl</a>(<span class="keyword">const</span> TensorInfo& weightInfo, <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout);</div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="comment"></span> </div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="comment">/// Weights for depthwise have a datalayout of [1,H,W,O] = [1,H,W,I*M]</span></div>
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="comment">/// This function coverts a TensorInfo from [1,H,W,I*M] to [1,I*M,H,W] (if NCHW) or keeps it at [1,H,W,I*M] (if NHWC)</span></div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="comment">/// as required by the compute library</span></div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="comment">/// Returns a tuple of converted weights tensor info and depth multiplier</span></div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="comment"></span>std::tuple<TensorInfo, unsigned int> <a class="code" href="namespacearmnn.html#ac4aa9e41515b354234645f115c49de32">Convert1HWOTensorInfoToAcl</a>(<span class="keyword">const</span> TensorInfo& weightInfo,</div>
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">const</span> TensorInfo& inputInfo,</div>
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout);</div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  </div>
<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <a class="code" href="classarmnn_1_1_const_tensor.html">armnn::ConstTensor</a> <a class="code" href="namespacearmnn.html#a8ca9f249dc67c111b8234b2c78d672cd">ConvertWeightTensorFromArmnnToAcl</a>(<span class="keyword">const</span> ConstTensorHandle* weightTensor,</div>
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout,</div>
<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordtype">void</span>* permuteBuffer);</div>
<div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="comment"></span> </div>
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span> <span class="comment">/// Weights for depthwise have a datalayout of [1,H,W,O] = [1,H,W,I*M]</span></div>
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="comment">/// This function coverts a ConstCpuTensorHandle from [1,H,W,I*M] to [1,I*M,H,W] (if NCHW) or</span></div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="comment">/// keeps it at [1,H,W,I*M] (if NHWC) as required by the compute library</span></div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="comment">///</span></div>
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="comment">/// \param weightTensor - ConstTensorHandle of weights tensor</span></div>
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="comment">/// \param inputInfo - TensorInfo of input tensor</span></div>
<div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="comment">/// \param dataLayout - DataLayout of the input tensor</span></div>
<div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="comment">/// \param permuteBuffer - Pointer to memory with the size of tensor. Used for the permutation</span></div>
<div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="comment">/// \return tuple of transformed weights-ConstTensor and depthwise multiplier</span></div>
<div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="comment"></span>std::tuple<ConstTensor, unsigned int> <a class="code" href="namespacearmnn.html#af35f79341ec6c10a8bd4c8caf0585ffb">Convert1HWOTensorToAcl</a>(<span class="keyword">const</span> ConstTensorHandle* weightTensor,</div>
<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">const</span> TensorInfo& inputInfo,</div>
<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout,</div>
<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keywordtype">void</span>* permuteBuffer);</div>
<div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="comment"></span> </div>
<div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="comment">/// Converts a (weights) tensor from [1, H, W, I*M] = [1, H, W, O] to [M, I, H, W]</span></div>
<div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="comment">///</span></div>
<div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="comment">/// \param weightTensor - ConstTensorHandle of the weight tensor that should be converted</span></div>
<div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="comment">/// \param inputInfo - TensorInfo of the corresponding input tensor</span></div>
<div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="comment">/// \param dataLayout - DataLayout of the input tensor e.g. NHWC or NCHW</span></div>
<div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="comment">/// \param permuteBuffer - Memory location with the same size as the weight tensor to write converted data to</span></div>
<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="comment">/// \return - A tuple of ConstTensor and unsigned int which is the converted weightTensor and the depthMultiplier</span></div>
<div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="comment"></span>std::tuple<ConstTensor, unsigned int> <a class="code" href="namespacearmnn.html#aa22a82f5240a0eb0d61135345080aa2d">Convert1HWOtoMIHW</a>(<span class="keyword">const</span> ConstTensorHandle* weightTensor,</div>
<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keyword">const</span> TensorInfo& inputInfo,</div>
<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a>& dataLayout,</div>
<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordtype">void</span>* permuteBuffer);</div>
<div class="line"><a name="l00268"></a><span class="lineno"> 268</span> <span class="comment"></span> </div>
<div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <span class="comment">/// Calculates the key index values needed for GatherNd: N, ND, K, W, C (N is always 1)</span></div>
<div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="comment">///</span></div>
<div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="comment">/// \param inputInfo0 - TensorInfo of the corresponding input tensor: params</span></div>
<div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="comment">/// \param inputInfo1 - TensorInfo of the corresponding input tensor: indices</span></div>
<div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="comment">/// \return - A map with names and values for N, ND, K, W, C</span></div>
<div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="comment"></span>std::map<std::string, unsigned int> <a class="code" href="namespacearmnn.html#ac40d3e4035af5fbe68d9e126a8d6367c">CalculateGatherNdKeyIndices</a>(TensorInfo inputInfo0, TensorInfo inputInfo1);</div>
<div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="comment"></span> </div>
<div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="comment">/// Generates a permutation vector of size rank that permutes the 2 most right dimensions</span></div>
<div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <span class="comment">///</span></div>
<div class="line"><a name="l00278"></a><span class="lineno"> 278</span> <span class="comment">/// \param rank - Tensor rank, i.e. number of dimensions in the tensors</span></div>
<div class="line"><a name="l00279"></a><span class="lineno"> 279</span> <span class="comment">/// \return - A permutation vector that permutes the 2 last dimensions</span></div>
<div class="line"><a name="l00280"></a><span class="lineno"> 280</span> <span class="comment"></span><a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a> <a class="code" href="namespacearmnn.html#a15e2ba06d2ecd7ff6013118838e5d1be">GeneratePermutationVectorOnLastTwoDimensions</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rank);</div>
<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  </div>
<div class="line"><a name="l00282"></a><span class="lineno"> 282</span> } <span class="comment">//namespace armnn</span></div>
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<div class="ttc" id="anamespacearmnn_html_ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360"><div class="ttname"><a href="namespacearmnn.html#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a></div><div class="ttdeci">@ Undefined</div></div>
<div class="ttc" id="anamespacearmnn_html_ac4aa9e41515b354234645f115c49de32"><div class="ttname"><a href="namespacearmnn.html#ac4aa9e41515b354234645f115c49de32">armnn::Convert1HWOTensorInfoToAcl</a></div><div class="ttdeci">std::tuple< TensorInfo, unsigned int > Convert1HWOTensorInfoToAcl(const TensorInfo &weightInfo, const TensorInfo &inputInfo, const DataLayout dataLayout)</div><div class="ttdoc">Weights for depthwise have a datalayout of [1,H,W,O] = [1,H,W,I*M] This function coverts a TensorInfo...</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.html#l00176">WorkloadUtils.cpp:176</a></div></div>
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