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<div class="title">ArmComputeTensorUtils.hpp</div> </div>
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<a href="_arm_compute_tensor_utils_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_tensor_8hpp.html">armnn/Tensor.hpp</a>&gt;</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_descriptors_fwd_8hpp.html">armnn/DescriptorsFwd.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &lt;arm_compute/core/ITensor.h&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="preprocessor">#include &lt;arm_compute/core/TensorInfo.h&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="preprocessor">#include &lt;arm_compute/core/Types.h&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="preprocessor">#include &lt;arm_compute/core/Size2D.h&gt;</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_half_8hpp.html">Half.hpp</a>&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="preprocessor">#include &lt;boost/cast.hpp&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;{</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="keyword">class </span>ITensorHandle;</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="keyword">namespace </span>armcomputetensorutils</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;{</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> GetArmComputeDataType(<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType, <span class="keywordtype">bool</span> multiScales);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> BuildArmComputeReductionCoordinates(<span class="keywordtype">size_t</span> inputDimensions,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originalInputRank,</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; armnnAxes);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>&amp; tensorShape);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> ConvertDataLayout(<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(<span class="keyword">const</span> Pooling2dDescriptor&amp; descriptor,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="keywordtype">bool</span> fpMixedPrecision = <span class="keyword">false</span>);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(<span class="keyword">const</span> NormalizationDescriptor&amp; desc);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;arm_compute::PermutationVector BuildArmComputePermutationVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a>&amp; vector);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;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="l00065"></a><span class="lineno"> 65</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;arm_compute::PixelValue GetPixelValue(arm_compute::ITensor&amp; input, <span class="keywordtype">float</span> pixelValue);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Descriptor&gt;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;arm_compute::PadStrideInfo BuildArmComputePadStrideInfo(<span class="keyword">const</span> Descriptor &amp;descriptor)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;{</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="keywordflow">return</span> arm_compute::PadStrideInfo(descriptor.m_StrideX,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; descriptor.m_StrideY,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; descriptor.m_PadLeft,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; descriptor.m_PadRight,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; descriptor.m_PadTop,</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; descriptor.m_PadBottom,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; arm_compute::DimensionRoundingType::FLOOR);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;}</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tensor&gt;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;<span class="keywordtype">void</span> BuildArmComputeTensor(Tensor&amp; tensor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo)</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;{</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; tensor.allocator()-&gt;init(BuildArmComputeTensorInfo(tensorInfo));</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160;}</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tensor&gt;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160;<span class="keywordtype">void</span> BuildArmComputeTensor(Tensor&amp; tensor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo, <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout)</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160;{</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; tensor.allocator()-&gt;init(BuildArmComputeTensorInfo(tensorInfo, dataLayout));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160;}</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tensor&gt;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;<span class="keywordtype">void</span> InitialiseArmComputeTensorEmpty(Tensor&amp; tensor)</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160;{</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; tensor.allocator()-&gt;allocate();</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;}</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Tensor&gt;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160;<span class="keywordtype">void</span> FreeTensorIfUnused(std::unique_ptr&lt;Tensor&gt;&amp; tensor)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160;{</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="keywordflow">if</span> (tensor &amp;&amp; !tensor-&gt;is_used())</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; {</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; tensor.reset(<span class="keyword">nullptr</span>);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160;}</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;<span class="comment">// Helper function to obtain byte offset into tensor data</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">size_t</span> GetTensorOffset(<span class="keyword">const</span> arm_compute::ITensorInfo&amp; <a class="code" href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; uint32_t depthIndex,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; uint32_t batchIndex,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; uint32_t channelIndex,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; uint32_t y,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; uint32_t x)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;{</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> coords;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; coords.set(4, static_cast&lt;int&gt;(depthIndex));</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; coords.set(3, static_cast&lt;int&gt;(batchIndex));</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; coords.set(2, static_cast&lt;int&gt;(channelIndex));</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; coords.set(1, static_cast&lt;int&gt;(y));</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; coords.set(0, static_cast&lt;int&gt;(x));</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keywordflow">return</span> boost::numeric_cast&lt;<span class="keywordtype">size_t</span>&gt;(info.offset_element_in_bytes(coords));</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;}</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160;<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="l00130"></a><span class="lineno"> 130</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">size_t</span> GetLinearBufferOffset(<span class="keyword">const</span> arm_compute::ITensorInfo&amp; info,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; uint32_t depthIndex,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; uint32_t batchIndex,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; uint32_t channelIndex,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; uint32_t y,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; uint32_t x)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160;{</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <span class="keyword">const</span> arm_compute::TensorShape&amp; shape = info.tensor_shape();</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; uint32_t width = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[0]);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; uint32_t height = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[1]);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; uint32_t numChannels = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[2]);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; uint32_t numBatches = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[3]);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="keywordflow">return</span> (((depthIndex * numBatches + batchIndex) * numChannels + channelIndex) * height + y) * width + x;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;}</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160;<span class="keywordtype">void</span> CopyArmComputeITensorData(<span class="keyword">const</span> arm_compute::ITensor&amp; srcTensor, T* dstData)</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160;{</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; <span class="comment">// If MaxNumOfTensorDimensions is increased, this loop will need fixing.</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; static_assert(<a class="code" href="namespacearmnn.html#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a> == 5, <span class="stringliteral">&quot;Please update CopyArmComputeITensorData&quot;</span>);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <span class="keyword">const</span> arm_compute::ITensorInfo&amp; info = *srcTensor.info();</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="keyword">const</span> arm_compute::TensorShape&amp; shape = info.tensor_shape();</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="keyword">const</span> uint8_t* <span class="keyword">const</span> bufferPtr = srcTensor.buffer();</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; uint32_t width = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[0]);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; uint32_t height = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[1]);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; uint32_t numChannels = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[2]);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; uint32_t numBatches = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[3]);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; uint32_t depth = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[4]);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthIndex = 0; depthIndex &lt; depth; ++depthIndex)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchIndex = 0; batchIndex &lt; numBatches; ++batchIndex)</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; {</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelIndex = 0; channelIndex &lt; numChannels; ++channelIndex)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; {</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y = 0; y &lt; height; ++y)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <span class="comment">// Copies one row from arm_compute tensor buffer to linear memory buffer.</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; <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="l00170"></a><span class="lineno"> 170</span>&#160; memcpy(</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; dstData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; width * <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; }</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160;}</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160;<span class="keywordtype">void</span> CopyArmComputeITensorData(<span class="keyword">const</span> T* srcData, arm_compute::ITensor&amp; dstTensor)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160;{</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <span class="comment">// If MaxNumOfTensorDimensions is increased, this loop will need fixing.</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; static_assert(<a class="code" href="namespacearmnn.html#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a> == 5, <span class="stringliteral">&quot;Please update CopyArmComputeITensorData&quot;</span>);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; <span class="keyword">const</span> arm_compute::ITensorInfo&amp; info = *dstTensor.info();</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <span class="keyword">const</span> arm_compute::TensorShape&amp; shape = info.tensor_shape();</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; uint8_t* <span class="keyword">const</span> bufferPtr = dstTensor.buffer();</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; uint32_t width = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[0]);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; uint32_t height = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[1]);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; uint32_t numChannels = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[2]);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; uint32_t numBatches = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[3]);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; uint32_t depth = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(shape[4]);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthIndex = 0; depthIndex &lt; depth; ++depthIndex)</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchIndex = 0; batchIndex &lt; numBatches; ++batchIndex)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelIndex = 0; channelIndex &lt; numChannels; ++channelIndex)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y = 0; y &lt; height; ++y)</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; {</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; <span class="comment">// Copies one row from linear memory buffer to arm_compute tensor buffer.</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; <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="l00206"></a><span class="lineno"> 206</span>&#160; memcpy(</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; srcData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0),</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; width * <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; }</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; }</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; }</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160;}</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> ArmComputeType, <span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160;TensorShape <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(<span class="keyword">const</span> ArmComputeType&amp; shapelike, T initial)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160;{</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; std::vector&lt;unsigned int&gt; s(<a class="code" href="namespacearmnn.html#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a>, initial);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i &lt; shapelike.num_dimensions(); ++i)</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; s[(shapelike.num_dimensions()-1)-i] = boost::numeric_cast&lt;unsigned int&gt;(shapelike[i]);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; }</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <span class="keywordflow">return</span> TensorShape(boost::numeric_cast&lt;unsigned int&gt;(shapelike.num_dimensions()), s.data());</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160;};</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160;<span class="keyword">inline</span> TensorShape GetStrides(<span class="keyword">const</span> arm_compute::Strides&amp; strides)</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160;{</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(strides, 0U);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160;}</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160;<span class="keyword">inline</span> TensorShape GetShape(<span class="keyword">const</span> arm_compute::TensorShape&amp; shape)</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160;{</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_utils.html#ab53d94ea22b51c6bcdf9584644bd67bb">GetTensorShape</a>(shape, 1U);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160;}</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160;} <span class="comment">// namespace armcomputetensorutils</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160;} <span class="comment">// namespace armnn</span></div><div class="ttc" id="namespacearmnn_utils_html_ab53d94ea22b51c6bcdf9584644bd67bb"><div class="ttname"><a href="namespacearmnn_utils.html#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.html#l00019">TensorUtils.cpp:19</a></div></div>
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