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<div class="title">ArmComputeTensorUtils.cpp</div> </div>
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<a href="_arm_compute_tensor_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> <span class="preprocessor">#include <<a class="code" href="_arm_compute_tensor_utils_8hpp.xhtml">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.xhtml">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.xhtml">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="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> {</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">namespace </span>armcomputetensorutils</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> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> GetArmComputeDataType(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType, <span class="keywordtype">bool</span> multiScales)</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> {</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keywordflow">switch</span>(dataType)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>:</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <span class="keywordflow">return</span> arm_compute::DataType::BFLOAT16;</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keywordflow">return</span> arm_compute::DataType::U8;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>:</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordflow">return</span> arm_compute::DataType::F16;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordflow">return</span> arm_compute::DataType::F32;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8_SIGNED;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">return</span> arm_compute::DataType::QSYMM16;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>:</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">return</span> arm_compute::DataType::S64;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">return</span> multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8;</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>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34346ec9593088efe3a29c0dad92166d">armnn::DataType::QuantizedSymm8PerAxis</a>:</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">return</span> arm_compute::DataType::QSYMM8_PER_CHANNEL;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">return</span> arm_compute::DataType::S32;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Unknown data type"</span>);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">return</span> arm_compute::DataType::UNKNOWN;</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> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <a class="code" href="namespacearmnn.xhtml#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> BuildArmComputeReductionCoordinates(<span class="keywordtype">size_t</span> inputDimensions,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originalInputRank,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> std::vector<unsigned int>& armnnAxes)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="namespacearmnn.xhtml#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> outAclCoords;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">if</span> (armnnAxes.empty())</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</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="l00061"></a><span class="lineno"> 61</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="l00062"></a><span class="lineno"> 62</span>  <span class="comment">// manually create a vector including all the input dimensions (in reversed order) as:</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">//</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 }</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="comment">//</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  outAclCoords.set_num_dimensions(inputDimensions);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () <span class="keyword">mutable</span> { <span class="keywordflow">return</span> d--; });</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</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="l00072"></a><span class="lineno"> 72</span>  <span class="comment">//</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</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="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// dimension correction).</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</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="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// new value for that reduction axis should be 1.</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">//</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="comment">// Example:</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="comment">// ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 }</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="comment">// ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 }</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 }</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">//</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">//</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  outAclCoords.set_num_dimensions(armnnAxes.size());</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::transform(armnnAxes.begin(), armnnAxes.end(),</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  outAclCoords.begin(),</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  [originalInputRank](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i){ <span class="keywordflow">return</span> originalInputRank - i - 1; });</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">return</span> outAclCoords;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> }</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& tensorShape)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  arm_compute::TensorShape shape;</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="comment">// armnn tensors are (batch, channels, height, width).</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// arm_compute tensors are (width, height, channels, batch).</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</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.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</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">// Note that our dimensions are stored in the opposite order to ACL's.</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  shape.set(tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - i - 1, tensorShape[i], <span class="keyword">false</span>);</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>  <span class="comment">// TensorShape::set() flattens leading ones, so that batch size 1 cannot happen.</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// arm_compute tensors expect this.</span></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>  <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  shape.set_num_dimensions(1);</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"> 115</span>  <span class="keywordflow">return</span> shape;</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> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</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="l00119"></a><span class="lineno"> 119</span> <span class="comment">// ARM Compute Tensor and CLTensor allocators.</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo)</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="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</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="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</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>  arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</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>  <span class="keywordflow">return</span> aclTensorInfo;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> ConvertDataLayout(<a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</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>  <span class="keywordflow">switch</span>(dataLayout)</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">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NHWC;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCHW;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</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="l00151"></a><span class="lineno"> 151</span>  std::to_string(static_cast<int>(dataLayout)) + <span class="stringliteral">"]"</span>);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(<span class="keyword">const</span> Pooling2dDescriptor& descriptor,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordtype">bool</span> fpMixedPrecision)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">using</span> arm_compute::PoolingType;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">using</span> arm_compute::DimensionRoundingType;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">using</span> arm_compute::PadStrideInfo;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">using</span> arm_compute::PoolingLayerInfo;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">using</span> arm_compute::Size2D;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a>;</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>  <span class="comment">// Resolve ARM Compute layer parameters.</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keyword">const</span> PoolingType poolingType = <a class="code" href="namespacearmnn.xhtml#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = ConvertDataLayout(descriptor.m_DataLayout);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 && descriptor.m_StrideY==0);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">//use specific constructor if global pooling</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">if</span>(isGlobalPooling)</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="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, dataLayout);</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> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">const</span> DimensionRoundingType rounding = <a class="code" href="namespacearmnn.xhtml#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  descriptor.m_OutputShapeRounding);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">const</span> PadStrideInfo padStrideInfo(descriptor.m_StrideX,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  descriptor.m_StrideY,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  descriptor.m_PadLeft,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  descriptor.m_PadRight,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  descriptor.m_PadTop,</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  descriptor.m_PadBottom,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  rounding);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</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>  <span class="keyword">const</span> Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight);</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> arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  fpMixedPrecision);</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> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(<span class="keyword">const</span> NormalizationDescriptor& descriptor)</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="keyword">const</span> arm_compute::NormType normType =</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="namespacearmnn.xhtml#aa5baabb8e3a4aa6cbdcab419d743e747">ConvertNormalizationAlgorithmChannelToAclNormType</a>(descriptor.m_NormChannelType);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordflow">return</span> arm_compute::NormalizationLayerInfo(normType,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  descriptor.m_NormSize,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  descriptor.m_Alpha,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  descriptor.m_Beta,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  descriptor.m_K,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">false</span>);</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> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> arm_compute::PermutationVector BuildArmComputePermutationVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>& perm)</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> {</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  arm_compute::PermutationVector aclPerm;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start = 0;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">while</span> ((start < perm.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>()) && (start == perm[start]))</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>  ++start;</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> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</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.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</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>  aclPerm.set(i - start, perm[i] - start);</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>  <span class="keywordflow">return</span> aclPerm;</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> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> arm_compute::PermutationVector BuildArmComputeTransposeVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>& perm)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> {</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  arm_compute::PermutationVector aclPerm;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  std::map<unsigned int, unsigned int> permuteMappings;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < perm.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  permuteMappings[perm[i]] = i;</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> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  std::vector<unsigned int> permuteVector;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < perm.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  permuteVector.push_back(permuteMappings.at(i));</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  }</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start = 0;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">while</span> ((start < perm.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>()) && (start == permuteVector[start]))</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>  ++start;</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> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</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.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  aclPerm.set(i - start, permuteVector[i] - start);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  }</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">return</span> aclPerm;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> }</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> 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="l00253"></a><span class="lineno"> 253</span> {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">return</span> arm_compute::Size2D(width, height);</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> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> arm_compute::PixelValue GetPixelValue(arm_compute::ITensor& input, <span class="keywordtype">float</span> pixelValue)</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>  <span class="keywordflow">switch</span> (input.info()->data_type())</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>  <span class="keywordflow">case</span> arm_compute::DataType::F16:</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast<Half>(pixelValue));</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">case</span> arm_compute::DataType::F32:</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(pixelValue);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8:</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast<uint8_t>(pixelValue));</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM16:</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast<int16_t>(pixelValue));</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8_SIGNED:</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8_PER_CHANNEL:</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast<int8_t>(pixelValue));</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordflow">case</span> arm_compute::DataType::S32:</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast<int32_t>(pixelValue));</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">throw</span> InvalidArgumentException(<span class="stringliteral">"Unsupported DataType: ["</span> +</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  std::to_string(static_cast<int>(input.info()->data_type())) + <span class="stringliteral">"]"</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> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> } <span class="comment">// namespace armcomputetensorutils</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> } <span class="comment">// namespace armnn</span></div><div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</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#l00050">Types.hpp:50</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#l00187">Tensor.hpp:187</a></div></div>
<div class="ttc" id="_arm_compute_utils_8hpp_xhtml"><div class="ttname"><a href="_arm_compute_utils_8hpp.xhtml">ArmComputeUtils.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad256fcf8c7f4d5a240fa47f0b56d50af"><div class="ttname"><a href="namespacearmnn.xhtml#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.xhtml#l00119">ArmComputeUtils.hpp:119</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a34346ec9593088efe3a29c0dad92166d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34346ec9593088efe3a29c0dad92166d">armnn::DataType::QuantizedSymm8PerAxis</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac6e86c1def7f674d3c4cb7f577874aa6"><div class="ttname"><a href="namespacearmnn.xhtml#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.xhtml#l00014">InternalTypes.hpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&#39;t count and are ignored. </div></div>
<div class="ttc" id="_arm_compute_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_arm_compute_tensor_utils_8hpp.xhtml">ArmComputeTensorUtils.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa5baabb8e3a4aa6cbdcab419d743e747"><div class="ttname"><a href="namespacearmnn.xhtml#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.xhtml#l00146">ArmComputeUtils.hpp:146</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_ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</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__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.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#l00241">Types.hpp:241</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8bc11f1fa23ef42532f9fdd04d355270"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#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.xhtml#l00442">Tensor.cpp:442</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_af672d1c9e2a120a18926cb645981fbb7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#af672d1c9e2a120a18926cb645981fbb7">armnn::TensorInfo::HasMultipleQuantizationScales</a></div><div class="ttdeci">bool HasMultipleQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00197">Tensor.hpp:197</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#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</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="namespacearmnn_xhtml_a8f3bfacadfd6d2146d6ccd299dabc7aa"><div class="ttname"><a href="namespacearmnn.xhtml#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.xhtml#l00132">ArmComputeUtils.hpp:132</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00469">Tensor.cpp:469</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00452">Tensor.cpp:452</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#l00194">Tensor.hpp:194</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_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</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#l00212">Types.hpp:212</a></div></div>
<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00174">Tensor.cpp:174</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="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
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