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<div class="title">WorkloadUtils.cpp</div> </div>
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<a href="_workload_utils_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="_workload_utils_8hpp.xhtml">backendsCommon/WorkloadUtils.hpp</a>></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="_utils_8hpp.xhtml">armnn/Utils.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <fmt/format.h></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <numeric></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></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"><a class="line" href="namespacearmnn.xhtml#aafe6180ef80d9f334f3a3ba9cc0db35d"> 18</a></span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> <a class="code" href="namespacearmnn.xhtml#aafe6180ef80d9f334f3a3ba9cc0db35d">PermuteTensor</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml">ConstTensorHandle</a>* tensor,</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>& permutationVector, <span class="keywordtype">void</span>* permuteBuffer)</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>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(tensor, <span class="stringliteral">"Invalid input tensor"</span>);</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(permuteBuffer, <span class="stringliteral">"Invalid permute buffer"</span>);</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo = tensor-><a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>();</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> (permutationVector.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>() > 0)</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>  tensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(tensorInfo, permutationVector);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnnUtils::Permute</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), permutationVector,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  tensor-><a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml#a3a76fc8b348e13d5a6ac1240c96ebef4">GetConstTensor</a><<span class="keywordtype">void</span>>(), permuteBuffer,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="namespacearmnn.xhtml#aa02b9e06fb20fa3c13da0427e6ee5ab2">GetDataTypeSize</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()));</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  }</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">else</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>  ::memcpy(permuteBuffer, tensor-><a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml#a3a76fc8b348e13d5a6ac1240c96ebef4">GetConstTensor</a><<span class="keywordtype">void</span>>(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  }</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, permuteBuffer);</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> </div><div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a3170fdd696155a247ecd81d445c0e2e1"> 41</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a3170fdd696155a247ecd81d445c0e2e1">ReshapeWeightsForAcl</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightInfo, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout)</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">// Reshape the weights in-place</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& weightShape = weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">switch</span> (dataLayout)</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>:</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// The data layout is NHWC, reshape from [ H, W, I, M ] to [ 1, H, W, I * M ]</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ 1,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  weightShape[0],</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  weightShape[1],</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  weightShape[2] * weightShape[3] });</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ 1,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  weightShape[0] * weightShape[1],</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  weightShape[2],</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  weightShape[3] });</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>:</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="comment">// The data layout is NCHW, reshape from [ M, I, H, W ] to [ 1, I * M, H, W, ]</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ 1, weightShape[0] * weightShape[1], weightShape[2], weightShape[3] });</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  }</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DataType></div><div class="line"><a name="l00067"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a52b301fd3adce20b51c4482cb52f1a38"> 67</a></span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> <a class="code" href="namespacearmnn.xhtml#a52b301fd3adce20b51c4482cb52f1a38">ReorderWeightChannelsForAcl</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>& weightHandle, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout, <span class="keywordtype">void</span>* permuteBuffer)</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>* weight = <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>*<span class="keyword">></span>(permuteBuffer);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& weightShape = weightHandle.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> multiplier;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">switch</span> (dataLayout)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>: <span class="comment">//It actually is [ H, W, I, M ]</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  height = weightShape[0];</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  width = weightShape[1];</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  inputChannels = weightShape[2];</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  multiplier = weightShape[3];</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>: <span class="comment">//It actually is [ M, I, H, W ]</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  height = weightShape[2];</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  width = weightShape[3];</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  inputChannels = weightShape[1];</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  multiplier = weightShape[0];</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::vector<DataType> weightAclOrder(height*width*inputChannels*multiplier);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> destinationWeightsChannel;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> totalChannels = inputChannels * multiplier;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelSize = height * width;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannel = 0;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originWeightsChannel = 0; originWeightsChannel < totalChannels; originWeightsChannel++)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  inputChannel = originWeightsChannel % inputChannels;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  destinationWeightsChannel = (originWeightsChannel - inputChannel) / inputChannels + multiplier * inputChannel;</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> i = 0; i < channelSize; i++)</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>  weightAclOrder[i + destinationWeightsChannel * channelSize] =</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  weight[i + originWeightsChannel * channelSize];</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  }</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  ::memcpy(permuteBuffer, weightAclOrder.data(), weightHandle.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(weightHandle.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(), permuteBuffer);</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> </div><div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a1e8288eac7e909fdb58b6113d816763a"> 115</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a1e8288eac7e909fdb58b6113d816763a">ConvertWeightTensorInfoFromArmnnToAcl</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightInfo, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout)</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> {</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// Convert the weight format from ArmNN's [ M, I, H, W ] (does NOT depend on the data layout) to either</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// [ 1, H, W, I * M ] (if NHWC) or [ 1, I * M, H, W ] (if NCHW), as required by the compute library</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// 1. Permute the weights if necessary</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// If the data layout is NCHW no permutation is necessary, as a reshape to [ 1, I * M, H, W ] can be better done</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// starting from the current shape of [ M, I, H, W ]</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightPermutedInfo(weightInfo);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// The data layout is NHWC, then permute the weights from [ M, I, H, W ] to [ H, W, I, M ]</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector{ 3, 2, 0, 1 };</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  weightPermutedInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(weightInfo, permutationVector);</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="comment">// 2. Reshape the weights</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="namespacearmnn.xhtml#a3170fdd696155a247ecd81d445c0e2e1">ReshapeWeightsForAcl</a>(weightPermutedInfo, dataLayout);</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>  <span class="comment">// 3. Return the permuted weight info</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">return</span> weightPermutedInfo;</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> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#af35f79341ec6c10a8bd4c8caf0585ffb"> 139</a></span> std::tuple<ConstTensor, unsigned int> <a class="code" href="namespacearmnn.xhtml#af35f79341ec6c10a8bd4c8caf0585ffb">Convert1HWOTensorToAcl</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml">ConstTensorHandle</a>* weightTensor,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordtype">void</span>* permuteBuffer)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo = weightTensor-><a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>();</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier = 1;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector{};</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  <span class="comment">// No permutation required. Data layouts are the same.</span></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>  depthMultiplier = weightsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] / inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>)</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="comment">// [ 1, H, W, I*M] --> [ 1, I * M, H, W ]</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  depthMultiplier = weightsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] / inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  permutationVector = { 0, 2, 3, 1 };</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  {</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"Unknown data layout for tensor conversion: {}"</span>,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="namespacearmnn.xhtml#aeef70b7611ae71e97ab55c75ef72b210">GetDataLayoutName</a>(dataLayout)));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  }</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightsPermuted = <a class="code" href="namespacearmnn.xhtml#aafe6180ef80d9f334f3a3ba9cc0db35d">PermuteTensor</a>(weightTensor, permutationVector, permuteBuffer);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordflow">return</span> std::make_tuple(weightsPermuted, depthMultiplier);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> }</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ac4aa9e41515b354234645f115c49de32"> 170</a></span> std::tuple<TensorInfo, unsigned int> <a class="code" href="namespacearmnn.xhtml#ac4aa9e41515b354234645f115c49de32">Convert1HWOTensorInfoToAcl</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightInfo,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout)</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclDepthMultiplier = 1;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsPermuted;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// No permutation required. Input and weights data layouts are the same.</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  aclDepthMultiplier = weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] / inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  weightsPermuted = weightInfo;</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> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>)</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>  <span class="comment">// Weights permutation required. Weights [N,H,W,C] and input [N,C,H,W] data layouts are different.</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// [ 1, H, W, I*M] --> [ 1, I * M, H, W ]</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  aclDepthMultiplier = weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] / inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector{ 0, 2, 3, 1 };</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  weightsPermuted = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(weightInfo, permutationVector);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  {</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"Unknown data layout for tensor info conversion: {}"</span>,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearmnn.xhtml#aeef70b7611ae71e97ab55c75ef72b210">GetDataLayoutName</a>(dataLayout)));</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">return</span> std::make_tuple(weightsPermuted, aclDepthMultiplier);</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> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aa22a82f5240a0eb0d61135345080aa2d"> 201</a></span> std::tuple<ConstTensor, unsigned int> <a class="code" href="namespacearmnn.xhtml#aa22a82f5240a0eb0d61135345080aa2d">Convert1HWOtoMIHW</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml">ConstTensorHandle</a>* weightTensor,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a>& dataLayout,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordtype">void</span>* permuteBuffer)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo = weightTensor-><a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>();</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">if</span> (weightsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">HasPerAxisQuantization</a>())</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Can't convert tensor from [1,H,W,Cout] to [M,Cin,H,W] when per channel "</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="stringliteral">"quantization is applied."</span>);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Reshape weights [ 1, H, W, I*M ] --> [ H, W, I, M ]</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keyword">auto</span> weightsShape = weightsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">auto</span> channelIndex = <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a>(dataLayout).<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier = weightsShape[3] / inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[channelIndex];</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  weightsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ weightsShape[1],</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  weightsShape[2],</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[channelIndex],</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  depthMultiplier});</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>  <span class="comment">// Permute [ H, W, I, M ] --> [ M, I, H, W ]</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector = { 2, 3, 1, 0 };</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightsPermuted = <a class="code" href="namespacearmnn.xhtml#aafe6180ef80d9f334f3a3ba9cc0db35d">PermuteTensor</a>(weightTensor, permutationVector, 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="keywordflow">return</span> std::make_tuple(weightsPermuted, depthMultiplier);</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> </div><div class="line"><a name="l00230"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a8ca9f249dc67c111b8234b2c78d672cd"> 230</a></span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> <a class="code" href="namespacearmnn.xhtml#a8ca9f249dc67c111b8234b2c78d672cd">ConvertWeightTensorFromArmnnToAcl</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml">ConstTensorHandle</a>* weightTensor,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordtype">void</span>* permuteBuffer)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> {</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(weightTensor, <span class="stringliteral">"Invalid input tensor"</span>);</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(permuteBuffer, <span class="stringliteral">"Invalid permute buffer"</span>);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">auto</span> multiplier = weightTensor-><a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">auto</span> inputChannels = weightTensor-><a class="code" href="classarmnn_1_1_const_tensor_handle.xhtml#a66e8f43a5b42b500871ed96e15419567">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="comment">// Convert the weight format from ArmNN's [ M, I, H, W ] (does NOT depend on the data layout) to either</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="comment">// [ 1, H, W, I * M ] (if NHWC) or [ 1, I * M, H, W ] (if NCHW), as required by the compute library</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> </div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// 1. Permute the weights if necessary</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="comment">// If the data layout is NCHW no permutation is necessary, as a reshape to [ 1, I * M, H, W ] can be better done</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="comment">// starting from the current shape of [ M, I, H, W ]</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="comment">// If no permutation is necessary, leave the permutation vector empty</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector{};</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="comment">// The data layout is NHWC, then permute the weights from [ M, I, H, W ] to [ H, W, I, M ]</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  permutationVector = { 3, 2, 0, 1 };</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightPermuted = <a class="code" href="namespacearmnn.xhtml#aafe6180ef80d9f334f3a3ba9cc0db35d">PermuteTensor</a>(weightTensor, permutationVector, permuteBuffer);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> </div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="comment">// Shuffle the weights data to obtain the channel order needed used by Acl</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">if</span> (multiplier > 1 && inputChannels > 1 && dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  {</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">switch</span> (weightPermuted.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>:</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  weightPermuted = ReorderWeightChannelsForAcl<float>(weightPermuted, dataLayout, permuteBuffer);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>:</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  weightPermuted =</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  ReorderWeightChannelsForAcl<half_float::half>(weightPermuted, dataLayout, permuteBuffer);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>:</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>:</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  weightPermuted = ReorderWeightChannelsForAcl<uint8_t>(weightPermuted, dataLayout, permuteBuffer);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>:</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  weightPermuted = ReorderWeightChannelsForAcl<int8_t>(weightPermuted, dataLayout, permuteBuffer);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  }</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="comment">// 2. Reshape the weights</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="namespacearmnn.xhtml#a3170fdd696155a247ecd81d445c0e2e1">ReshapeWeightsForAcl</a>(weightPermuted.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(), dataLayout);</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">// 3. Return both the tensor and the allocated storage to ensure that the data stays alive</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordflow">return</span> weightPermuted;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> }</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ad69ffa576a596b9eb20ab6a41420c541"> 286</a></span> int32_t <a class="code" href="namespacearmnn.xhtml#ad69ffa576a596b9eb20ab6a41420c541">ConvertMaskToACLFormat</a>(int32_t mask, int32_t numDim)</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> {</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  int32_t reversedMask = 0;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < armnn::numeric_cast<unsigned int>(numDim); ++i)</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// Check if bit set in mask for each dimension</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  int32_t bit = (mask & 1 << i) != 0;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="comment">// Increment the new mask with the bits reversed</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  reversedMask += (bit << std::max(numDim-(armnn::numeric_cast<int>(i)+1), 0));</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  }</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">return</span> reversedMask;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ac40d3e4035af5fbe68d9e126a8d6367c"> 300</a></span> std::map<std::string, unsigned int> <a class="code" href="namespacearmnn.xhtml#ac40d3e4035af5fbe68d9e126a8d6367c">CalculateGatherNdKeyIndices</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo0, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo1)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> {</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  std::vector<unsigned int> paramsShape;</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputInfo0.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  {</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  paramsShape.push_back(inputInfo0.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  }</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> </div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  std::vector<unsigned int> indicesShape;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputInfo1.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  {</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  indicesShape.push_back(inputInfo1.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  }</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  std::map<std::string, unsigned int> keyIndices;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="comment">// N: number of batches</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  keyIndices[<span class="stringliteral">"N"</span>] = 1;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="comment">// ND: number of dimensions that are sliced from params</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  keyIndices[<span class="stringliteral">"ND"</span>] = indicesShape.back();</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="comment">// W: number of indices in each batch (all but the last dimension)</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  keyIndices[<span class="stringliteral">"W"</span>] =</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(std::accumulate(std::begin(indicesShape),</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  std::end(indicesShape) - 1,</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  1,</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  std::multiplies<>() ));</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="comment">// K: range of each index</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  keyIndices[<span class="stringliteral">"K"</span>] =</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(std::accumulate(std::begin(paramsShape),</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  std::begin(paramsShape) + static_cast<int>(keyIndices[<span class="stringliteral">"ND"</span>]),</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  1,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  std::multiplies<>() ));</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="comment">// C: number of channels for each index</span></div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  keyIndices[<span class="stringliteral">"C"</span>] =</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(std::accumulate(std::begin(paramsShape) + static_cast<int>(keyIndices[<span class="stringliteral">"ND"</span>]),</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  std::end(paramsShape),</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  1,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  std::multiplies<>() ));</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">return</span> keyIndices;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> }</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> } <span class="comment">// namespace armnn</span></div><div class="ttc" id="namespacearmnn_xhtml_aafe6180ef80d9f334f3a3ba9cc0db35d"><div class="ttname"><a href="namespacearmnn.xhtml#aafe6180ef80d9f334f3a3ba9cc0db35d">armnn::PermuteTensor</a></div><div class="ttdeci">armnn::ConstTensor PermuteTensor(const ConstTensorHandle *tensor, const PermutationVector &permutationVector, void *permuteBuffer)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00018">WorkloadUtils.cpp:18</a></div></div>
<div class="ttc" id="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aeef70b7611ae71e97ab55c75ef72b210"><div class="ttname"><a href="namespacearmnn.xhtml#aeef70b7611ae71e97ab55c75ef72b210">armnn::GetDataLayoutName</a></div><div class="ttdeci">constexpr const char * GetDataLayoutName(DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00222">TypesUtils.hpp:222</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00062">Types.hpp:62</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#l00191">Tensor.hpp:191</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac40d3e4035af5fbe68d9e126a8d6367c"><div class="ttname"><a href="namespacearmnn.xhtml#ac40d3e4035af5fbe68d9e126a8d6367c">armnn::CalculateGatherNdKeyIndices</a></div><div class="ttdeci">std::map< std::string, unsigned int > CalculateGatherNdKeyIndices(TensorInfo inputInfo0, TensorInfo inputInfo1)</div><div class="ttdoc">Calculates the key index values needed for GatherNd: N, ND, K, W, C (N is always 1) ...</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00300">WorkloadUtils.cpp:300</a></div></div>
<div class="ttc" id="_utils_8hpp_xhtml"><div class="ttname"><a href="_utils_8hpp.xhtml">Utils.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a1e8288eac7e909fdb58b6113d816763a"><div class="ttname"><a href="namespacearmnn.xhtml#a1e8288eac7e909fdb58b6113d816763a">armnn::ConvertWeightTensorInfoFromArmnnToAcl</a></div><div class="ttdeci">TensorInfo ConvertWeightTensorInfoFromArmnnToAcl(const TensorInfo &weightInfo, DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00115">WorkloadUtils.cpp:115</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#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_ab85cd8cc10c96a7c99c14042c251fc48"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">armnn::TensorInfo::HasPerAxisQuantization</a></div><div class="ttdeci">bool HasPerAxisQuantization() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00446">Tensor.cpp:446</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8ca9f249dc67c111b8234b2c78d672cd"><div class="ttname"><a href="namespacearmnn.xhtml#a8ca9f249dc67c111b8234b2c78d672cd">armnn::ConvertWeightTensorFromArmnnToAcl</a></div><div class="ttdeci">armnn::ConstTensor ConvertWeightTensorFromArmnnToAcl(const ConstTensorHandle *weightTensor, DataLayout dataLayout, void *permuteBuffer)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00230">WorkloadUtils.cpp:230</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::BaseTensor::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00297">Tensor.hpp:297</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abcbdfb544ece4c31d0b37715ad0f3be0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">armnn::TensorInfo::GetNumBytes</a></div><div class="ttdeci">unsigned int GetNumBytes() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00427">Tensor.cpp:427</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00338">Types.hpp:338</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a52b301fd3adce20b51c4482cb52f1a38"><div class="ttname"><a href="namespacearmnn.xhtml#a52b301fd3adce20b51c4482cb52f1a38">armnn::ReorderWeightChannelsForAcl</a></div><div class="ttdeci">ConstTensor ReorderWeightChannelsForAcl(const ConstTensor &weightHandle, DataLayout dataLayout, void *permuteBuffer)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00067">WorkloadUtils.cpp:67</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor_handle.xhtml">armnn::ConstTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_tensor_handle_8hpp_source.xhtml#l00024">TensorHandle.hpp:24</a></div></div>
<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00193">Tensor.hpp:193</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_handle_xhtml_a66e8f43a5b42b500871ed96e15419567"><div class="ttname"><a href="classarmnn_1_1_const_tensor_handle.xhtml#a66e8f43a5b42b500871ed96e15419567">armnn::ConstTensorHandle::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_tensor_handle_8hpp_source.xhtml#l00040">TensorHandle.hpp:40</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_af35f79341ec6c10a8bd4c8caf0585ffb"><div class="ttname"><a href="namespacearmnn.xhtml#af35f79341ec6c10a8bd4c8caf0585ffb">armnn::Convert1HWOTensorToAcl</a></div><div class="ttdeci">std::tuple< ConstTensor, unsigned int > Convert1HWOTensorToAcl(const ConstTensorHandle *weightTensor, const TensorInfo &inputInfo, const DataLayout dataLayout, void *permuteBuffer)</div><div class="ttdoc">Weights for depthwise have a datalayout of [1,H,W,O] = [1,H,W,I*M] This function coverts a ConstCpuTe...</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00139">WorkloadUtils.cpp:139</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00048">Types.hpp:48</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00198">Tensor.hpp:198</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac4aa9e41515b354234645f115c49de32"><div class="ttname"><a href="namespacearmnn.xhtml#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.xhtml#l00170">WorkloadUtils.cpp:170</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">armnn::BaseTensor::GetInfo</a></div><div class="ttdeci">const TensorInfo & GetInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00295">Tensor.hpp:295</a></div></div>
<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00295">Types.hpp:295</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad69ffa576a596b9eb20ab6a41420c541"><div class="ttname"><a href="namespacearmnn.xhtml#ad69ffa576a596b9eb20ab6a41420c541">armnn::ConvertMaskToACLFormat</a></div><div class="ttdeci">int32_t ConvertMaskToACLFormat(int32_t mask, int32_t numDim)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00286">WorkloadUtils.cpp:286</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa22a82f5240a0eb0d61135345080aa2d"><div class="ttname"><a href="namespacearmnn.xhtml#aa22a82f5240a0eb0d61135345080aa2d">armnn::Convert1HWOtoMIHW</a></div><div class="ttdeci">std::tuple< ConstTensor, unsigned int > Convert1HWOtoMIHW(const ConstTensorHandle *weightTensor, const TensorInfo &inputInfo, const DataLayout &dataLayout, void *permuteBuffer)</div><div class="ttdoc">Converts a (weights) tensor from [1, H, W, I*M] = [1, H, W, O] to [M, I, H, W]. </div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00201">WorkloadUtils.cpp:201</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00514">Tensor.cpp:514</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">armnn::BaseTensor::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00300">Tensor.hpp:300</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3170fdd696155a247ecd81d445c0e2e1"><div class="ttname"><a href="namespacearmnn.xhtml#a3170fdd696155a247ecd81d445c0e2e1">armnn::ReshapeWeightsForAcl</a></div><div class="ttdeci">void ReshapeWeightsForAcl(TensorInfo &weightInfo, DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00041">WorkloadUtils.cpp:41</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00195">Tensor.hpp:195</a></div></div>
<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">armnnUtils::DataLayoutIndexed::GetChannelsIndex</a></div><div class="ttdeci">unsigned int GetChannelsIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00023">DataLayoutIndexed.hpp:23</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00098">Permute.cpp:98</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_handle_xhtml_a3a76fc8b348e13d5a6ac1240c96ebef4"><div class="ttname"><a href="classarmnn_1_1_const_tensor_handle.xhtml#a3a76fc8b348e13d5a6ac1240c96ebef4">armnn::ConstTensorHandle::GetConstTensor</a></div><div class="ttdeci">const T * GetConstTensor() const</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_tensor_handle_8hpp_source.xhtml#l00028">TensorHandle.hpp:28</a></div></div>
<div class="ttc" id="_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_utils_8hpp.xhtml">WorkloadUtils.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa02b9e06fb20fa3c13da0427e6ee5ab2"><div class="ttname"><a href="namespacearmnn.xhtml#aa02b9e06fb20fa3c13da0427e6ee5ab2">armnn::GetDataTypeSize</a></div><div class="ttdeci">constexpr unsigned int GetDataTypeSize(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00151">TypesUtils.hpp:151</a></div></div>
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