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<div class="title">ArmComputeTensorUtils.cpp</div> </div>
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<a href="_arm_compute_tensor_utils_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#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">#include &lt;<a class="code" href="_arm_compute_tensor_utils_8hpp.html">aclCommon/ArmComputeTensorUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_utils_8hpp.html">aclCommon/ArmComputeUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_exceptions_8hpp.html">armnn/Exceptions.hpp</a>&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_descriptors_8hpp.html">armnn/Descriptors.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;{</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="keyword">namespace </span>armcomputetensorutils</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;</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</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="l00017"></a><span class="lineno"> 17</span>&#160;{</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160; <span class="keywordflow">switch</span>(dataType)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160; {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::U8;</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>:</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::F16;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::F32;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8_SIGNED;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::QSYMM16;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="keywordflow">return</span> multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; }</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <a class="code" href="_deprecated_8hpp.html#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a34346ec9593088efe3a29c0dad92166d">armnn::DataType::QuantizedSymm8PerAxis</a>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::QSYMM8_PER_CHANNEL;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; <a class="code" href="_deprecated_8hpp.html#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::S32;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; BOOST_ASSERT_MSG(<span class="keyword">false</span>, <span class="stringliteral">&quot;Unknown data type&quot;</span>);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <span class="keywordflow">return</span> arm_compute::DataType::UNKNOWN;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; }</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;}</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</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="l00049"></a><span class="lineno"> 49</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originalInputRank,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; armnnAxes)</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;{</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> outAclCoords;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; <span class="keywordflow">if</span> (armnnAxes.empty())</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <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="l00057"></a><span class="lineno"> 57</span>&#160; <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="l00058"></a><span class="lineno"> 58</span>&#160; <span class="comment">// manually create a vector including all the input dimensions (in reversed order) as:</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="comment">// { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 }</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; outAclCoords.set_num_dimensions(inputDimensions);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; 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="l00064"></a><span class="lineno"> 64</span>&#160; }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="comment">// Create a vector of reduction dimensions (in reversed order) with the given reduction axes.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <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="l00070"></a><span class="lineno"> 70</span>&#160; <span class="comment">// dimension correction).</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; <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="l00072"></a><span class="lineno"> 72</span>&#160; <span class="comment">// new value for that reduction axis should be 1.</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="comment">// Example:</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <span class="comment">// ArmNN input shape = { 1, 1, 3, 2 } -&gt; ACL input shape = { 2, 3 }</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="comment">// ArmNN reduction axis = { 2 } -&gt; ACL reduction axis = { 1 }</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="comment">// ArmNN reduction axis = { 3 } -&gt; ACL reduction axis = { 0 }</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <span class="comment">// The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="comment">//</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; outAclCoords.set_num_dimensions(armnnAxes.size());</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; std::transform(armnnAxes.begin(), armnnAxes.end(),</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; outAclCoords.begin(),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; [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="l00085"></a><span class="lineno"> 85</span>&#160; }</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="keywordflow">return</span> outAclCoords;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;}</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</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="l00091"></a><span class="lineno"> 91</span>&#160;{</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; arm_compute::TensorShape shape;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="comment">// armnn tensors are (batch, channels, height, width).</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; <span class="comment">// arm_compute tensors are (width, height, channels, batch).</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; {</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="comment">// Note that our dimensions are stored in the opposite order to ACL&#39;s.</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; shape.set(tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - i - 1, tensorShape[i], <span class="keyword">false</span>);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="comment">// TensorShape::set() flattens leading ones, so that batch size 1 cannot happen.</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="comment">// arm_compute tensors expect this.</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</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; shape.set_num_dimensions(1);</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; <span class="keywordflow">return</span> shape;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;}</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160;<span class="comment">// Utility function used to build a TensorInfo object, that can be used to initialise</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160;<span class="comment">// ARM Compute Tensor and CLTensor allocators.</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</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="l00117"></a><span class="lineno"> 117</span>&#160;{</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <span class="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <span class="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keywordflow">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</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;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="l00130"></a><span class="lineno"> 130</span>&#160; <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160;{</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keywordflow">return</span> aclTensorInfo;</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;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</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="l00139"></a><span class="lineno"> 139</span>&#160;{</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <span class="keywordflow">switch</span>(dataLayout)</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; {</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NHWC;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCHW;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; <span class="keywordflow">default</span>: <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unknown armnn::DataLayout: [&quot;</span> +</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; std::to_string(static_cast&lt;int&gt;(dataLayout)) + <span class="stringliteral">&quot;]&quot;</span>);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; }</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160;}</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;arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(<span class="keyword">const</span> Pooling2dDescriptor&amp; descriptor,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="keywordtype">bool</span> fpMixedPrecision)</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160;{</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; <span class="keyword">using</span> arm_compute::PoolingType;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="keyword">using</span> arm_compute::DimensionRoundingType;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; <span class="keyword">using</span> arm_compute::PadStrideInfo;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; <span class="keyword">using</span> arm_compute::PoolingLayerInfo;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="keyword">using</span> arm_compute::Size2D;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; <span class="keyword">using</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a>;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <span class="comment">// Resolve ARM Compute layer parameters.</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="keyword">const</span> PoolingType poolingType = <a class="code" href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</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="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = ConvertDataLayout(descriptor.m_DataLayout);</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="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 &amp;&amp; descriptor.m_StrideY==0);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <span class="comment">//use specific constructor if global pooling</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <span class="keywordflow">if</span>(isGlobalPooling)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; <span class="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, dataLayout);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; }</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <span class="keyword">const</span> DimensionRoundingType rounding = <a class="code" href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; descriptor.m_OutputShapeRounding);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; <span class="keyword">const</span> PadStrideInfo padStrideInfo(descriptor.m_StrideX,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; descriptor.m_StrideY,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; descriptor.m_PadLeft,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; descriptor.m_PadRight,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; descriptor.m_PadTop,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; descriptor.m_PadBottom,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; rounding);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <span class="keyword">const</span> Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight);</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="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; fpMixedPrecision);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160;}</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160;arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(<span class="keyword">const</span> NormalizationDescriptor&amp; descriptor)</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160;{</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; <span class="keyword">const</span> arm_compute::NormType normType =</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; <a class="code" href="namespacearmnn.html#aa5baabb8e3a4aa6cbdcab419d743e747">ConvertNormalizationAlgorithmChannelToAclNormType</a>(descriptor.m_NormChannelType);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <span class="keywordflow">return</span> arm_compute::NormalizationLayerInfo(normType,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; descriptor.m_NormSize,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; descriptor.m_Alpha,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; descriptor.m_Beta,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; descriptor.m_K,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="keyword">false</span>);</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;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</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; perm)</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160;{</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; arm_compute::PermutationVector aclPerm;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start = 0;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="keywordflow">while</span> ((start &lt; perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>()) &amp;&amp; (start == perm[start]))</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; ++start;</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; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = start; i &lt; perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</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; aclPerm.set(i - start, perm[i] - start);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; }</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <span class="keywordflow">return</span> aclPerm;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160;}</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</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="l00222"></a><span class="lineno"> 222</span>&#160;{</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <span class="keywordflow">return</span> arm_compute::Size2D(width, height);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160;}</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;arm_compute::PixelValue GetPixelValue(arm_compute::ITensor&amp; input, <span class="keywordtype">float</span> pixelValue)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160;{</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <span class="keywordflow">switch</span> (input.info()-&gt;data_type())</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; <span class="keywordflow">case</span> arm_compute::DataType::F16:</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast&lt;Half&gt;(pixelValue));</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; <span class="keywordflow">case</span> arm_compute::DataType::F32:</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; <span class="keywordflow">return</span> arm_compute::PixelValue(pixelValue);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8:</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast&lt;uint8_t&gt;(pixelValue));</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; <span class="keywordflow">case</span> arm_compute::DataType::QSYMM16:</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast&lt;int16_t&gt;(pixelValue));</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8_PER_CHANNEL:</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; <span class="keywordflow">return</span> arm_compute::PixelValue(static_cast&lt;int8_t&gt;(pixelValue));</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unsupported DataType: [&quot;</span> +</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; std::to_string(static_cast&lt;int&gt;(input.info()-&gt;data_type())) + <span class="stringliteral">&quot;]&quot;</span>);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; }</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="classarmnn_1_1_tensor_info_html_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00264">Tensor.cpp:264</a></div></div>
<div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00043">Tensor.hpp:43</a></div></div>
<div class="ttc" id="_exceptions_8hpp_html"><div class="ttname"><a href="_exceptions_8hpp.html">Exceptions.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_html"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00170">Types.hpp:170</a></div></div>
<div class="ttc" id="namespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div>
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<div class="ttc" id="_arm_compute_tensor_utils_8hpp_html"><div class="ttname"><a href="_arm_compute_tensor_utils_8hpp.html">ArmComputeTensorUtils.hpp</a></div></div>
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<div class="ttc" id="_arm_compute_utils_8hpp_html"><div class="ttname"><a href="_arm_compute_utils_8hpp.html">ArmComputeUtils.hpp</a></div></div>
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