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<div class="title">ArmComputeTensorUtils.cpp</div> </div>
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<a href="_arm_compute_tensor_utils_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017-2024 Arm Ltd and Contributors. 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> <span class="preprocessor">#include <<a class="code" href="_arm_compute_tensor_utils_8hpp.html">aclCommon/ArmComputeTensorUtils.hpp</a>></span></div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <<a class="code" href="_arm_compute_utils_8hpp.html">aclCommon/ArmComputeUtils.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="_exceptions_8hpp.html">armnn/Exceptions.hpp</a>"</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "<a class="code" href="_arm_compute_utils_8hpp.html">ArmComputeUtils.hpp</a>"</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.html">armnn/Descriptors.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>  </div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> {</div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="keyword">namespace </span>armcomputetensorutils</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>  </div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> GetArmComputeDataType(<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType, <span class="keywordtype">bool</span> multiScales)</div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> {</div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <span class="keywordflow">switch</span>(dataType)</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="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>:</div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordflow">return</span> arm_compute::DataType::BFLOAT16;</div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordflow">return</span> arm_compute::DataType::U8;</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>:</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordflow">return</span> arm_compute::DataType::F16;</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">return</span> arm_compute::DataType::F32;</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8_SIGNED;</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8;</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">return</span> arm_compute::DataType::QSYMM16;</div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>:</div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">return</span> arm_compute::DataType::S64;</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  {</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">return</span> multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8;</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="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordflow">return</span> arm_compute::DataType::S32;</div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">default</span>:</div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Unknown data type"</span>);</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">return</span> arm_compute::DataType::UNKNOWN;</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  }</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span> }</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  </div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> GetArmNNDataType(<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> dataType)</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> {</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">switch</span>(dataType)</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">case</span> arm_compute::DataType::BFLOAT16:</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>;</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">case</span> arm_compute::DataType::U8:</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>;</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">case</span> arm_compute::DataType::F16:</div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>;</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">case</span> arm_compute::DataType::F32:</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8_SIGNED:</div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>;</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8:</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>;</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM16:</div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">case</span> arm_compute::DataType::S64:</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>;</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8_PER_CHANNEL:</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>;</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8:</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>;</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">case</span> arm_compute::DataType::S32:</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">default</span>:</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Unknown data type"</span>);</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> }</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  </div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> BuildArmComputeReductionCoordinates(<span class="keywordtype">size_t</span> inputDimensions,</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originalInputRank,</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> std::vector<unsigned int>& armnnAxes)</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>  <a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> outAclCoords;</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  </div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">if</span> (armnnAxes.empty())</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>  <span class="comment">// If no reduction axes were provided, then the input must be reduced along all dimensions.</span></div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="comment">// Since Compute Library does not accept an empty vector as the reduction dimensions, we then</span></div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// manually create a vector including all the input dimensions (in reversed order) as:</span></div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 }</span></div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  outAclCoords.set_num_dimensions(inputDimensions);</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () <span class="keyword">mutable</span> { return d--; });</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>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Create a vector of reduction dimensions (in reversed order) with the given reduction axes.</span></div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any</span></div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// dimension correction).</span></div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the</span></div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="comment">// new value for that reduction axis should be 1.</span></div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="comment">// Example:</span></div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">// ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 }</span></div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="comment">// ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 }</span></div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 }</span></div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1</span></div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">//</span></div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  outAclCoords.set_num_dimensions(armnnAxes.size());</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  std::transform(armnnAxes.begin(), armnnAxes.end(),</div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  outAclCoords.begin(),</div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  [originalInputRank](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i){ return originalInputRank - i - 1; });</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</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">return</span> outAclCoords;</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>  </div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span> arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>& tensorShape)</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span> {</div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  arm_compute::TensorShape shape;</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  </div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// armnn tensors are (batch, channels, height, width).</span></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// arm_compute tensors are (width, height, channels, batch).</span></div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="comment">// Note that our dimensions are stored in the opposite order to ACL's.</span></div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  shape.set(tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - i - 1, tensorShape[i], <span class="keyword">false</span>);</div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  </div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// TensorShape::set() flattens leading ones, so that batch size 1 cannot happen.</span></div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// arm_compute tensors expect this.</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"> 139</span>  </div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</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>  shape.set_num_dimensions(1);</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  </div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">return</span> shape;</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>  </div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span> std::vector<unsigned int> ReduceDimsForACL(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a> tensorShape, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</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>  std::vector<unsigned int> newShape;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimsToSkip = 0;</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="keywordflow">if</span> (tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > dimensions)</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>  dimsToSkip = tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - dimensions;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimsSkipped = 0;</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">bool</span> insertRemainder = <span class="keyword">false</span>;</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</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>  <span class="keywordflow">if</span> (tensorShape[i] == 1 && dimsSkipped < dimsToSkip && !insertRemainder)</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>  ++dimsSkipped;</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordflow">continue</span>;</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>  newShape.insert(newShape.begin(), tensorShape[i]);</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// Once we insert the first dimension we can't skip any more</span></div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  insertRemainder = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">return</span> newShape;</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span> }</div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  </div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>& tensorShape, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</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>  arm_compute::TensorShape shape;</div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  std::vector<unsigned int> strippedShape = ReduceDimsForACL(tensorShape, dimensions);</div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  </div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < strippedShape.size(); i++)</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>  shape.set(i, strippedShape[i], <span class="keyword">false</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>  </div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</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>  shape.set_num_dimensions(1);</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">return</span> shape;</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>  </div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment">// Utility function used to build a TensorInfo object, that can be used to initialise</span></div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment">// ARM Compute Tensor and CLTensor allocators.</span></div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment">// Note: this utility ignores the value of armnn::TensorInfo.IsConstant(). ACL tensors</span></div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment">// default to constant but Arm NN ones default to non constant. In the cases where</span></div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="comment">// we expect ACL to treat a tensor as constant that value must be set after this</span></div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="comment">// utility has been called.</span></div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span> arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& tensorInfo)</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>  <span class="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</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>  <span class="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div>
<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset());</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">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</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>  </div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span> arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& tensorInfo,</div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span> {</div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo);</div>
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</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>  <span class="keywordflow">return</span> aclTensorInfo;</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>  </div>
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span> arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& tensorInfo, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> {</div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), dimensions);</div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  </div>
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div>
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset());</div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  </div>
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</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> arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& tensorInfo,</div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</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>  arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo, dimensions);</div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</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="keywordflow">return</span> aclTensorInfo;</div>
<div class="line"><a name="l00241"></a><span class="lineno"> 241</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>  </div>
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> ConvertDataLayout(<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span> {</div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">switch</span>(dataLayout)</div>
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  {</div>
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NHWC;</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="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCHW;</div>
<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  </div>
<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NDHWC;</div>
<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  </div>
<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCDHW;</div>
<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  </div>
<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">default</span>: <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">"Unknown armnn::DataLayout: ["</span> +</div>
<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  std::to_string(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(dataLayout)) + <span class="stringliteral">"]"</span>);</div>
<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  }</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>  </div>
<div class="line"><a name="l00261"></a><span class="lineno"> 261</span> arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.html#a7e75f47f676327bce37149932aa4a011">Pooling2dDescriptor</a>& descriptor,</div>
<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordtype">bool</span> fpMixedPrecision)</div>
<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> {</div>
<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="comment">// Resolve ARM Compute layer parameters.</span></div>
<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keyword">const</span> arm_compute::PoolingType poolingType = <a class="code" href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</div>
<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  </div>
<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> dataLayout = ConvertDataLayout(descriptor.m_DataLayout);</div>
<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  </div>
<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 && descriptor.m_StrideY==0);</div>
<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="comment">//use specific constructor if global pooling</span></div>
<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">if</span>(isGlobalPooling)</div>
<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  {</div>
<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, dataLayout);</div>
<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  }</div>
<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  </div>
<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">const</span> arm_compute::DimensionRoundingType rounding = <a class="code" href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div>
<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  descriptor.m_OutputShapeRounding);</div>
<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> arm_compute::PadStrideInfo padStrideInfo(descriptor.m_StrideX,</div>
<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  descriptor.m_StrideY,</div>
<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  descriptor.m_PadLeft,</div>
<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  descriptor.m_PadRight,</div>
<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  descriptor.m_PadTop,</div>
<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  descriptor.m_PadBottom,</div>
<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  rounding);</div>
<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  </div>
<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</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>  <span class="keyword">const</span> arm_compute::Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight);</div>
<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  </div>
<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding,</div>
<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  fpMixedPrecision);</div>
<div class="line"><a name="l00292"></a><span class="lineno"> 292</span> }</div>
<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  </div>
<div class="line"><a name="l00294"></a><span class="lineno"> 294</span> arm_compute::Pooling3dLayerInfo BuildArmComputePooling3dLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.html#a6713b8a83104db317823b5367b195d2e">Pooling3dDescriptor</a>& descriptor,</div>
<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordtype">bool</span> fpMixedPrecision)</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="keyword">const</span> arm_compute::PoolingType poolingType = <a class="code" href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</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>  <span class="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 && descriptor.m_StrideY==0 && descriptor.m_StrideZ==0);</div>
<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="comment">//use specific constructor if global pooling</span></div>
<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keywordflow">if</span>(isGlobalPooling)</div>
<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  {</div>
<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordflow">return</span> arm_compute::Pooling3dLayerInfo(poolingType);</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>  </div>
<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keyword">const</span> arm_compute::Size3D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight, descriptor.m_PoolDepth);</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>  <span class="keyword">const</span> arm_compute::Size3D stride(descriptor.m_StrideX,</div>
<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  descriptor.m_StrideY,</div>
<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  descriptor.m_StrideZ);</div>
<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  </div>
<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">const</span> arm_compute::Padding3D padding(descriptor.m_PadLeft,</div>
<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  descriptor.m_PadRight,</div>
<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  descriptor.m_PadTop,</div>
<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  descriptor.m_PadBottom,</div>
<div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  descriptor.m_PadFront,</div>
<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  descriptor.m_PadBack);</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="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</div>
<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  </div>
<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> arm_compute::DimensionRoundingType rounding = <a class="code" href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div>
<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  descriptor.m_OutputShapeRounding);</div>
<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  </div>
<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">return</span> arm_compute::Pooling3dLayerInfo(poolingType,</div>
<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  poolSize,</div>
<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  stride,</div>
<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  padding,</div>
<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  excludePadding,</div>
<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  fpMixedPrecision,</div>
<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  rounding);</div>
<div class="line"><a name="l00331"></a><span class="lineno"> 331</span> }</div>
<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  </div>
<div class="line"><a name="l00333"></a><span class="lineno"> 333</span> arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(<span class="keyword">const</span> NormalizationDescriptor& descriptor)</div>
<div class="line"><a name="l00334"></a><span class="lineno"> 334</span> {</div>
<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keyword">const</span> arm_compute::NormType normType =</div>
<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="namespacearmnn.html#aa5baabb8e3a4aa6cbdcab419d743e747">ConvertNormalizationAlgorithmChannelToAclNormType</a>(descriptor.m_NormChannelType);</div>
<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keywordflow">return</span> arm_compute::NormalizationLayerInfo(normType,</div>
<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  descriptor.m_NormSize,</div>
<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  descriptor.m_Alpha,</div>
<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  descriptor.m_Beta,</div>
<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  descriptor.m_K,</div>
<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">false</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>  </div>
<div class="line"><a name="l00345"></a><span class="lineno"> 345</span> arm_compute::PermutationVector BuildArmComputePermutationVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a>& perm)</div>
<div class="line"><a name="l00346"></a><span class="lineno"> 346</span> {</div>
<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  arm_compute::PermutationVector aclPerm;</div>
<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  </div>
<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start = 0;</div>
<div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">while</span> ((start < perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>()) && (start == perm[start]))</div>
<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  {</div>
<div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  ++start;</div>
<div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div>
<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  </div>
<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = start; i < perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div>
<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  {</div>
<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  aclPerm.set(i - start, perm[i] - start);</div>
<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  }</div>
<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">return</span> aclPerm;</div>
<div class="line"><a name="l00360"></a><span class="lineno"> 360</span> }</div>
<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  </div>
<div class="line"><a name="l00362"></a><span class="lineno"> 362</span> arm_compute::PermutationVector BuildArmComputeTransposeVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a>& perm)</div>
<div class="line"><a name="l00363"></a><span class="lineno"> 363</span> {</div>
<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="comment">// As ArmNN indexes are left to right and ACL indexes are right to left,</span></div>
<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="comment">// the permutation vector has to be reversed and then translated into ACL axis.</span></div>
<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// i.e. {1, 0, 2, 3} --> {3, 2, 0, 1} --> {0, 1, 3, 2}</span></div>
<div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  </div>
<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="comment">// Below an example of how the ArmNN and ACL index format work:</span></div>
<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="comment">// ArmNN Format:</span></div>
<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="comment">// Input Shape {1, 10, 20, 30}</span></div>
<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="comment">// Permutation Vector {1, 0, 2, 3}</span></div>
<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// Output Shape {10, 1, 20, 30}</span></div>
<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="comment">// dim "1" of input goes into index 0 of the output ([ 10, X, X, X])</span></div>
<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="comment">// dim "0" of input goes into index 1 of the output ([ 10, 1, X, X ])</span></div>
<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="comment">// dim "2" of input goes into index 2 of the output ([ 10, 1, 20, X ])</span></div>
<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="comment">// dim "3" of input goes into index 3 of the output ([ 10, 1, 20, 30 ])</span></div>
<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="comment">// ACL Format:</span></div>
<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="comment">// Input Shape {30, 20, 10, 1}</span></div>
<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="comment">// Permutation Vector {0, 1, 3, 2}</span></div>
<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="comment">// Output Shape {30, 20, 1, 10}</span></div>
<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="comment">// dim "0" of input goes into index 0 of the output ([ 30, X, X, X])</span></div>
<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="comment">// dim "1" of input goes into index 1 of the output ([ 30, 20, X, X ])</span></div>
<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="comment">// dim "3" of input goes into index 2 of the output ([ 30, 20, 1, X ])</span></div>
<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="comment">// dim "2" of input goes into index 3 of the output ([ 30, 20, 1, 10 ])</span></div>
<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  </div>
<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  arm_compute::PermutationVector aclPerm;</div>
<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keyword">auto</span> rank = perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>();</div>
<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  </div>
<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="comment">// Reverse the order. i.e. {1, 0, 2, 3} --> {3, 2, 0, 1}</span></div>
<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  std::vector<unsigned int> reversedPerm;</div>
<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  reversedPerm.reserve(rank);</div>
<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = rank; i > 0; --i)</div>
<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  {</div>
<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  reversedPerm.push_back(perm[i-1]);</div>
<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  }</div>
<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  </div>
<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="comment">// Translate from Arm NN axis to ACL axis. i.e. {3, 2, 0, 1} --> {0, 1, 3, 2}</span></div>
<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < rank; ++i)</div>
<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  {</div>
<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">auto</span> aclAxis = rank - 1 - reversedPerm[i];</div>
<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  aclPerm.set(i, aclAxis);</div>
<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  }</div>
<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keywordflow">return</span> aclPerm;</div>
<div class="line"><a name="l00404"></a><span class="lineno"> 404</span> }</div>
<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  </div>
<div class="line"><a name="l00406"></a><span class="lineno"> 406</span> arm_compute::Size2D BuildArmComputeSize2D(<span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height)</div>
<div class="line"><a name="l00407"></a><span class="lineno"> 407</span> {</div>
<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">return</span> arm_compute::Size2D(width, height);</div>
<div class="line"><a name="l00409"></a><span class="lineno"> 409</span> }</div>
<div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  </div>
<div class="line"><a name="l00411"></a><span class="lineno"> 411</span> arm_compute::PixelValue GetPixelValue(<span class="keyword">const</span> arm_compute::ITensorInfo* tensorInfo, <span class="keywordtype">float</span> value)</div>
<div class="line"><a name="l00412"></a><span class="lineno"> 412</span> {</div>
<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keywordflow">switch</span> (tensorInfo->data_type())</div>
<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  {</div>
<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keywordflow">case</span> arm_compute::DataType::F16:</div>
<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  {</div>
<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  arm_compute::PixelValue pixelValue = arm_compute::PixelValue(<span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a><span class="keyword">></span>(value));</div>
<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordflow">if</span> (isinf(pixelValue.get<<a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a>>())) {</div>
<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">"Under/Overflow converting float value ["</span> + std::to_string(value) +</div>
<div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="stringliteral">"] to fp16: ["</span> + std::to_string(pixelValue.get<<a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a>>()) + <span class="stringliteral">"]"</span>);</div>
<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  }</div>
<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keywordflow">return</span> pixelValue;</div>
<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  }</div>
<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keywordflow">case</span> arm_compute::DataType::F32:</div>
<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(value);</div>
<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8:</div>
<div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast<</span>uint8_t<span class="keyword">></span>(value));</div>
<div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM16:</div>
<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast<</span>int16_t<span class="keyword">></span>(value));</div>
<div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8:</div>
<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8_SIGNED:</div>
<div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8_PER_CHANNEL:</div>
<div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast<</span>int8_t<span class="keyword">></span>(value));</div>
<div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keywordflow">case</span> arm_compute::DataType::S32:</div>
<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(value));</div>
<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keywordflow">default</span>:</div>
<div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">"Unsupported DataType: ["</span> +</div>
<div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  std::to_string(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(tensorInfo->data_type())) + <span class="stringliteral">"]"</span>);</div>
<div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  }</div>
<div class="line"><a name="l00440"></a><span class="lineno"> 440</span> }</div>
<div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  </div>
<div class="line"><a name="l00442"></a><span class="lineno"> 442</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ComputeDepthwiseConv2dDepthMultiplier(<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div>
<div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keyword">const</span> arm_compute::TensorShape& weightsShape,</div>
<div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keyword">const</span> arm_compute::TensorShape& inputShape)</div>
<div class="line"><a name="l00445"></a><span class="lineno"> 445</span> {</div>
<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier;</div>
<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div>
<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  {</div>
<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  depthMultiplier = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(weightsShape[0]) / <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(inputShape[0]);</div>
<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  }</div>
<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>)</div>
<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  {</div>
<div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  depthMultiplier = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(weightsShape[2]) / <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(inputShape[2]);</div>
<div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div>
<div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div>
<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordflow">throw</span> InvalidArgumentException(fmt::format(<span class="stringliteral">"Unknown data layout for tensor conversion: {}"</span>,</div>
<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="namespacearmnn.html#aeef70b7611ae71e97ab55c75ef72b210">GetDataLayoutName</a>(layout)));</div>
<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  }</div>
<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">return</span> depthMultiplier;</div>
<div class="line"><a name="l00461"></a><span class="lineno"> 461</span> }</div>
<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  </div>
<div class="line"><a name="l00463"></a><span class="lineno"> 463</span> } <span class="comment">// namespace armcomputetensorutils</span></div>
<div class="line"><a name="l00464"></a><span class="lineno"> 464</span> } <span class="comment">// namespace armnn</span></div>
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<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div><div class="ttdeci">@ Boolean</div></div>
<div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a></div><div class="ttdeci">@ NCDHW</div></div>
<div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00062">Types.hpp:62</a></div></div>
<div class="ttc" id="a_descriptors_8hpp_html"><div class="ttname"><a href="_descriptors_8hpp.html">Descriptors.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a8f3bfacadfd6d2146d6ccd299dabc7aa"><div class="ttname"><a href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">armnn::ConvertOutputShapeRoundingToAclDimensionRoundingType</a></div><div class="ttdeci">arm_compute::DimensionRoundingType ConvertOutputShapeRoundingToAclDimensionRoundingType(OutputShapeRounding rounding)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00168">ArmComputeUtils.hpp:168</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8bc11f1fa23ef42532f9fdd04d355270"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">armnn::TensorInfo::GetQuantizationScales</a></div><div class="ttdeci">std::vector< float > GetQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00451">Tensor.cpp:451</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div><div class="ttdeci">@ NHWC</div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div><div class="ttdeci">@ Float32</div></div>
<div class="ttc" id="anamespacearmnn_html_aeef70b7611ae71e97ab55c75ef72b210"><div class="ttname"><a href="namespacearmnn.html#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.html#l00253">TypesUtils.hpp:253</a></div></div>
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<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div><div class="ttdeci">@ QSymmS8</div></div>
<div class="ttc" id="anamespacearmnn_html_a0b49aa352b84d572942185ce72cef751"><div class="ttname"><a href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">armnn::Half</a></div><div class="ttdeci">half_float::half Half</div><div class="ttdef"><b>Definition:</b> <a href="_half_8hpp_source.html#l00022">Half.hpp:22</a></div></div>
<div class="ttc" id="a_assert_8hpp_html_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.html#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.html#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ac6e86c1def7f674d3c4cb7f577874aa6"><div class="ttname"><a href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">armnn::Coordinates</a></div><div class="ttdeci">std::array< unsigned int, MaxNumOfTensorDimensions > Coordinates</div><div class="ttdef"><b>Definition:</b> <a href="_internal_types_8hpp_source.html#l00015">InternalTypes.hpp:15</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div><div class="ttdeci">@ QSymmS16</div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div><div class="ttdeci">@ BFloat16</div></div>
<div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a></div><div class="ttdeci">@ NDHWC</div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_shape_html"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div><div class="ttdeci">@ Float16</div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_shape_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00174">Tensor.cpp:174</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdeci">@ Exclude</div><div class="ttdoc">The padding fields don't count and are ignored.</div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_af672d1c9e2a120a18926cb645981fbb7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">armnn::TensorInfo::HasMultipleQuantizationScales</a></div><div class="ttdeci">bool HasMultipleQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00203">Tensor.hpp:203</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00048">Types.hpp:48</a></div></div>
<div class="ttc" id="a_arm_compute_utils_8hpp_html"><div class="ttname"><a href="_arm_compute_utils_8hpp.html">ArmComputeUtils.hpp</a></div></div>
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<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00200">Tensor.hpp:200</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div><div class="ttdeci">@ Signed32</div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div><div class="ttdeci">@ QAsymmS8</div></div>
<div class="ttc" id="aclassarmnn_1_1_permutation_vector_html_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00357">Types.hpp:357</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00193">Tensor.hpp:193</a></div></div>
<div class="ttc" id="anamespacearmnn_deserializer_html_a6713b8a83104db317823b5367b195d2e"><div class="ttname"><a href="namespacearmnn_deserializer.html#a6713b8a83104db317823b5367b195d2e">armnnDeserializer::Pooling3dDescriptor</a></div><div class="ttdeci">const armnnSerializer::Pooling3dDescriptor * Pooling3dDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.html#l00022">Deserializer.hpp:22</a></div></div>
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<div class="ttc" id="anamespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">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.html#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="anamespacearmnn_html_aa5baabb8e3a4aa6cbdcab419d743e747"><div class="ttname"><a href="namespacearmnn.html#aa5baabb8e3a4aa6cbdcab419d743e747">armnn::ConvertNormalizationAlgorithmChannelToAclNormType</a></div><div class="ttdeci">arm_compute::NormType ConvertNormalizationAlgorithmChannelToAclNormType(NormalizationAlgorithmChannel channelType)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00182">ArmComputeUtils.hpp:182</a></div></div>
<div class="ttc" id="a_arm_compute_tensor_utils_8hpp_html"><div class="ttname"><a href="_arm_compute_tensor_utils_8hpp.html">ArmComputeTensorUtils.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a></div><div class="ttdeci">@ Signed64</div></div>
<div class="ttc" id="anamespacearmnn_deserializer_html_a7e75f47f676327bce37149932aa4a011"><div class="ttname"><a href="namespacearmnn_deserializer.html#a7e75f47f676327bce37149932aa4a011">armnnDeserializer::Pooling2dDescriptor</a></div><div class="ttdeci">const armnnSerializer::Pooling2dDescriptor * Pooling2dDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.html#l00021">Deserializer.hpp:21</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div><div class="ttdeci">@ NCHW</div></div>
<div class="ttc" id="anamespacearmnn_html_ad256fcf8c7f4d5a240fa47f0b56d50af"><div class="ttname"><a href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">armnn::ConvertPoolingAlgorithmToAclPoolingType</a></div><div class="ttdeci">arm_compute::PoolingType ConvertPoolingAlgorithmToAclPoolingType(PoolingAlgorithm poolingAlgorithm)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00155">ArmComputeUtils.hpp:155</a></div></div>
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