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<div class="title">ArmComputeTensorUtils.hpp</div> </div>
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<a href="_arm_compute_tensor_utils_8hpp.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 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">#pragma once</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_tensor_8hpp.xhtml">armnn/Tensor.hpp</a>></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_fwd_8hpp.xhtml">armnn/DescriptorsFwd.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <arm_compute/core/ITensor.h></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <arm_compute/core/TensorInfo.h></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <arm_compute/core/Types.h></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> <span class="preprocessor">#include <<a class="code" href="_half_8hpp.xhtml">Half.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="keyword">class </span>ITensorHandle;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">namespace </span>armcomputetensorutils</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment"></span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment">/// Utility function to map an armnn::DataType to corresponding arm_compute::DataType.</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment"></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="l00027"></a><span class="lineno"> 27</span> <span class="comment"></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment">/// Utility function to map an arm_compute::DataType to corresponding armnn::DataType.</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> GetArmNNDataType(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> datatype);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment"></span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="comment">/// Utility function used to set up an arm_compute::Coordinates from a vector of ArmNN Axes for reduction functions</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment"></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="l00033"></a><span class="lineno"> 33</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originalInputRank,</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keyword">const</span> std::vector<unsigned int>& armnnAxes);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="comment"></span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="comment">/// Utility function used to setup an arm_compute::TensorShape object from an armnn::TensorShape.</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="comment"></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="l00038"></a><span class="lineno"> 38</span> <span class="comment"></span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment">/// Utility function used to setup an arm_compute::ITensorInfo object whose dimensions are based on the given</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment">/// armnn::ITensorInfo.</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment"></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="l00042"></a><span class="lineno"> 42</span> <span class="comment"></span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment">/// Utility function used to setup an arm_compute::ITensorInfo object whose dimensions are based on the given</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment">/// armnn::ITensorInfo.</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment">/// armnn::DataLayout.</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment"></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="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment"></span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="comment">/// Utility function used to convert armnn::DataLayout to arm_compute::DataLayout</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment">/// armnn::DataLayout.</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment"></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="l00052"></a><span class="lineno"> 52</span> <span class="comment"></span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment">/// Utility function used to setup an arm_compute::PoolingLayerInfo object from given</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="comment">/// armnn::Pooling2dDescriptor</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment">/// bool fpMixedPrecision</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment"></span>arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.xhtml#a7e75f47f676327bce37149932aa4a011">Pooling2dDescriptor</a>& descriptor,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordtype">bool</span> fpMixedPrecision = <span class="keyword">false</span>);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="comment"></span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="comment">/// Utility function used to setup an arm_compute::Pooling3dLayerInfo object from given</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="comment">/// armnn::Pooling3dDescriptor</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="comment">/// bool fpMixedPrecision</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="comment"></span>arm_compute::Pooling3dLayerInfo BuildArmComputePooling3dLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.xhtml#a6713b8a83104db317823b5367b195d2e">Pooling3dDescriptor</a>& descriptor,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordtype">bool</span> fpMixedPrecision = <span class="keyword">false</span>);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="comment"></span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="comment">/// Utility function to setup an arm_compute::NormalizationLayerInfo object from an armnn::NormalizationDescriptor.</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="comment"></span>arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(<span class="keyword">const</span> NormalizationDescriptor& desc);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="comment"></span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="comment">/// Utility function used to setup an arm_compute::PermutationVector object from an armnn::PermutationVector.</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="comment"></span>arm_compute::PermutationVector BuildArmComputePermutationVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>& vector);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="comment"></span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="comment">/// Utility function used to setup an arm_compute::PermutationVector object from an armnn::PermutationVector.</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="comment"></span>arm_compute::PermutationVector BuildArmComputeTransposeVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>& vector);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="comment"></span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="comment">/// Utility function used to setup an arm_compute::Size2D object from width and height values.</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="comment"></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="l00076"></a><span class="lineno"> 76</span> <span class="comment"></span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="comment">/// Gets the appropriate PixelValue for the TensorInfo DataType</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="comment"></span>arm_compute::PixelValue GetPixelValue(<span class="keyword">const</span> arm_compute::ITensorInfo* tensorInfo, <span class="keywordtype">float</span> pixelValue);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="comment"></span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="comment">/// Computes the depth multiplier parameter for the Depthwise Conv2d ACL workload.</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment"></span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ComputeDepthwiseConv2dDepthMultiplier(<a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">const</span> arm_compute::TensorShape& weightsShape,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">const</span> arm_compute::TensorShape& inputShape);</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> <span class="comment">/// Utility function used to setup an arm_compute::PadStrideInfo object from an armnn layer descriptor.</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> Descriptor></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> arm_compute::PadStrideInfo BuildArmComputePadStrideInfo(<span class="keyword">const</span> Descriptor &descriptor)</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">return</span> arm_compute::PadStrideInfo(descriptor.m_StrideX,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  descriptor.m_StrideY,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  descriptor.m_PadLeft,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  descriptor.m_PadRight,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  descriptor.m_PadTop,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  descriptor.m_PadBottom,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  arm_compute::DimensionRoundingType::FLOOR);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="comment"></span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="comment">/// Sets up the given ArmCompute tensor's dimensions based on the given ArmNN tensor.</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> Tensor></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="keywordtype">void</span> BuildArmComputeTensor(Tensor& tensor, <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="l00101"></a><span class="lineno"> 101</span> {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  tensor.allocator()->init(BuildArmComputeTensorInfo(tensorInfo));</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="comment"></span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="comment">/// Sets up the given ArmCompute tensor's dimensions based on the given ArmNN tensor.</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> Tensor></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="keywordtype">void</span> BuildArmComputeTensor(Tensor& tensor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout)</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>  tensor.allocator()->init(BuildArmComputeTensorInfo(tensorInfo, dataLayout));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Tensor></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="keywordtype">void</span> InitialiseArmComputeTensorEmpty(Tensor& tensor)</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>  tensor.allocator()->allocate();</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"></span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment">/// Utility function to free unused tensors after a workload is configured and prepared</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> Tensor></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="keywordtype">void</span> FreeTensorIfUnused(std::unique_ptr<Tensor>& tensor)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">if</span> (tensor && !tensor->is_used())</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>  tensor.reset(<span class="keyword">nullptr</span>);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment">// Helper function to obtain byte offset into tensor data</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="keyword">inline</span> <span class="keywordtype">size_t</span> GetTensorOffset(<span class="keyword">const</span> arm_compute::ITensorInfo& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  uint32_t depthIndex,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  uint32_t batchIndex,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  uint32_t channelIndex,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  uint32_t y,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  uint32_t x)</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>  <a class="code" href="namespacearmnn.xhtml#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> coords;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  coords.set(4, static_cast<int>(depthIndex));</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  coords.set(3, static_cast<int>(batchIndex));</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  coords.set(2, static_cast<int>(channelIndex));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  coords.set(1, static_cast<int>(y));</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  coords.set(0, static_cast<int>(x));</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">size_t</span>>(info.offset_element_in_bytes(coords));</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment">// Helper function to obtain element offset into data buffer representing tensor data (assuming no strides).</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="keyword">inline</span> <span class="keywordtype">size_t</span> GetLinearBufferOffset(<span class="keyword">const</span> arm_compute::ITensorInfo& info,</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  uint32_t depthIndex,</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  uint32_t batchIndex,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  uint32_t channelIndex,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  uint32_t y,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  uint32_t x)</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="keyword">const</span> arm_compute::TensorShape& shape = info.tensor_shape();</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  uint32_t width = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[0]);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  uint32_t height = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[1]);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  uint32_t numChannels = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[2]);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  uint32_t numBatches = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[3]);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">return</span> (((depthIndex * numBatches + batchIndex) * numChannels + channelIndex) * height + y) * width + x;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="keywordtype">void</span> CopyArmComputeITensorData(<span class="keyword">const</span> arm_compute::ITensor& srcTensor, T* dstData)</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="comment">// If MaxNumOfTensorDimensions is increased, this loop will need fixing.</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  static_assert(<a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a> == 5, <span class="stringliteral">"Please update CopyArmComputeITensorData"</span>);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keyword">const</span> arm_compute::ITensorInfo& info = *srcTensor.info();</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">const</span> arm_compute::TensorShape& shape = info.tensor_shape();</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> uint8_t* <span class="keyword">const</span> bufferPtr = srcTensor.buffer();</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  uint32_t width = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[0]);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  uint32_t height = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[1]);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  uint32_t numChannels = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[2]);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  uint32_t numBatches = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[3]);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  uint32_t depth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[4]);</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthIndex = 0; depthIndex < depth; ++depthIndex)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchIndex = 0; batchIndex < numBatches; ++batchIndex)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelIndex = 0; channelIndex < numChannels; ++channelIndex)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y = 0; y < height; ++y)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="comment">// Copies one row from arm_compute tensor buffer to linear memory buffer.</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="comment">// A row is the largest contiguous region we can copy, as the tensor data may be using strides.</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  memcpy(</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  dstData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  width * <span class="keyword">sizeof</span>(T));</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>  }</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>  }</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="keywordtype">void</span> CopyArmComputeITensorData(<span class="keyword">const</span> T* srcData, arm_compute::ITensor& dstTensor)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="comment">// If MaxNumOfTensorDimensions is increased, this loop will need fixing.</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  static_assert(<a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a> == 5, <span class="stringliteral">"Please update CopyArmComputeITensorData"</span>);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> arm_compute::ITensorInfo& info = *dstTensor.info();</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">const</span> arm_compute::TensorShape& shape = info.tensor_shape();</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  uint8_t* <span class="keyword">const</span> bufferPtr = dstTensor.buffer();</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  uint32_t width = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[0]);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  uint32_t height = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[1]);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  uint32_t numChannels = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[2]);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  uint32_t numBatches = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[3]);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  uint32_t depth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(shape[4]);</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthIndex = 0; depthIndex < depth; ++depthIndex)</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchIndex = 0; batchIndex < numBatches; ++batchIndex)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelIndex = 0; channelIndex < numChannels; ++channelIndex)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y = 0; y < height; ++y)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="comment">// Copies one row from linear memory buffer to arm_compute tensor buffer.</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">// A row is the largest contiguous region we can copy, as the tensor data may be using strides.</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  memcpy(</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  srcData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  width * <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  }</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  }</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  }</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="comment"></span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="comment">/// Construct a TensorShape object from an ArmCompute object based on arm_compute::Dimensions.</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="comment">/// \tparam ArmComputeType Any type that implements the Dimensions interface</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="comment">/// \tparam T Shape value type</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="comment">/// \param shapelike An ArmCompute object that implements the Dimensions interface</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="comment">/// \param initial A default value to initialise the shape with</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="comment">/// \return A TensorShape object filled from the Acl shapelike object.</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="comment"></span><span class="keyword">template</span><<span class="keyword">typename</span> ArmComputeType, <span class="keyword">typename</span> T></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> TensorShape <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(<span class="keyword">const</span> ArmComputeType& shapelike, T initial)</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>  std::vector<unsigned int> s(<a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a>, initial);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i < shapelike.num_dimensions(); ++i)</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>  s[(shapelike.num_dimensions()-1)-i] = armnn::numeric_cast<unsigned int>(shapelike[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>  <span class="keywordflow">return</span> TensorShape(armnn::numeric_cast<unsigned int>(shapelike.num_dimensions()), s.data());</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="comment"></span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="comment">/// Get the strides from an ACL strides object</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="comment"></span><span class="keyword">inline</span> TensorShape GetStrides(<span class="keyword">const</span> arm_compute::Strides& strides)</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> {</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(strides, 0U);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> }</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="comment"></span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="comment">/// Get the shape from an ACL shape object</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="comment"></span><span class="keyword">inline</span> TensorShape GetShape(<span class="keyword">const</span> arm_compute::TensorShape& shape)</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">return</span> <a class="code" href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(shape, 1U);</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> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> } <span class="comment">// namespace armcomputetensorutils</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> } <span class="comment">// namespace armnn</span></div><div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00062">Types.hpp:62</a></div></div>
<div class="ttc" id="_tensor_8hpp_xhtml"><div class="ttname"><a href="_tensor_8hpp.xhtml">Tensor.hpp</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_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#l00015">InternalTypes.hpp:15</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="namespacearmnn_deserializer_xhtml_a7e75f47f676327bce37149932aa4a011"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#a7e75f47f676327bce37149932aa4a011">armnnDeserializer::Pooling2dDescriptor</a></div><div class="ttdeci">const armnnSerializer::Pooling2dDescriptor * Pooling2dDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.xhtml#l00021">Deserializer.hpp:21</a></div></div>
<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00048">Types.hpp:48</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.xhtml#ab53d94ea22b51c6bcdf9584644bd67bb">armnnUtils::GetTensorShape</a></div><div class="ttdeci">armnn::TensorShape GetTensorShape(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00019">TensorUtils.cpp:19</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00295">Types.hpp:295</a></div></div>
<div class="ttc" id="_half_8hpp_xhtml"><div class="ttname"><a href="_half_8hpp.xhtml">Half.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00035">NumericCast.hpp:35</a></div></div>
<div class="ttc" id="_descriptors_fwd_8hpp_xhtml"><div class="ttname"><a href="_descriptors_fwd_8hpp.xhtml">DescriptorsFwd.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_abdcd184ed3bd648bb31d385040cafd5d"><div class="ttname"><a href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">armnn::MaxNumOfTensorDimensions</a></div><div class="ttdeci">constexpr unsigned int MaxNumOfTensorDimensions</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00031">Types.hpp:31</a></div></div>
<div class="ttc" id="namespacearmnn_deserializer_xhtml_a6713b8a83104db317823b5367b195d2e"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#a6713b8a83104db317823b5367b195d2e">armnnDeserializer::Pooling3dDescriptor</a></div><div class="ttdeci">const armnnSerializer::Pooling3dDescriptor * Pooling3dDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.xhtml#l00022">Deserializer.hpp:22</a></div></div>
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