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<div class="title">NeonGatherNdWorkload.cpp</div> </div>
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<a href="_neon_gather_nd_workload_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2022 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> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_neon_gather_nd_workload_8hpp.xhtml">NeonGatherNdWorkload.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_neon_workload_utils_8hpp.xhtml">NeonWorkloadUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_polymorphic_downcast_8hpp.xhtml">armnn/utility/PolymorphicDowncast.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_arm_compute_utils_8hpp.xhtml">aclCommon/ArmComputeUtils.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "<a class="code" href="_workload_utils_8hpp.xhtml">backendsCommon/WorkloadUtils.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="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> {</div><div class="line"><a name="l00014"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aec41b8c86e61ce02a07b8215bf8bc073"> 14</a></span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> <a class="code" href="namespacearmnn.xhtml#aec41b8c86e61ce02a07b8215bf8bc073">NeonGatherNdWorkloadValidate</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& paramsInfo,</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& indicesInfo,</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo)</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="comment">// Calculate ND, K, W, C.</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  std::map<std::string, unsigned int> keyIndices = <a class="code" href="namespacearmnn.xhtml#ac40d3e4035af5fbe68d9e126a8d6367c">CalculateGatherNdKeyIndices</a>(paramsInfo, indicesInfo);</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> /// Validate Mul</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"></span> <span class="comment">// Indices with shape { W, ND }</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indices_W_ND_Info = indicesInfo;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  indices_W_ND_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"W"</span>], keyIndices[<span class="stringliteral">"ND"</span>] });</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclIndicesInfo = BuildArmComputeTensorInfo(indices_W_ND_Info);</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="comment">// Flattened coefficients with shape { ND }</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> flattenedCoeff_Info = indicesInfo;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  flattenedCoeff_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"ND"</span>] });</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclFlattenedCoeffInfo = BuildArmComputeTensorInfo(flattenedCoeff_Info);</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// Output of Mul with shape { W, ND }</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputMulInfo = BuildArmComputeTensorInfo(indices_W_ND_Info);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keyword">auto</span> statusMul = arm_compute::NEPixelWiseMultiplication::validate(&aclIndicesInfo,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  &aclFlattenedCoeffInfo,</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  &aclOutputMulInfo,</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  1.0f,</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  arm_compute::ConvertPolicy::WRAP,</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  arm_compute::RoundingPolicy::TO_ZERO,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  arm_compute::ActivationLayerInfo());</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"> /// Validate ReduceSum</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"></span> <span class="comment">// Flattened indices with shape { W }</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> flattenedIndices_Info = indicesInfo;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  flattenedIndices_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"W"</span>] });</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclFlattenedIndicesInfo = BuildArmComputeTensorInfo(flattenedIndices_Info);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">const</span> std::vector<unsigned int> armnnReduceAxes(1, 1);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="namespacearmnn.xhtml#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> coords = BuildArmComputeReductionCoordinates(aclOutputMulInfo.num_dimensions(),</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  indices_W_ND_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(),</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  armnnReduceAxes);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">auto</span> statusReduceSum = arm_compute::NEReductionOperation::validate(&aclOutputMulInfo,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  &aclFlattenedIndicesInfo,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  static_cast<unsigned int>(coords[0]),</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  arm_compute::ReductionOperation::SUM,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">false</span>);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="comment"></span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="comment"> /// Validate Gather</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="comment"></span> <span class="comment">// Params with shape { K, C }</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> params_K_C_Info = paramsInfo;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  params_K_C_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"K"</span>], keyIndices[<span class="stringliteral">"C"</span>] });</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclParamsInfo = BuildArmComputeTensorInfo(params_K_C_Info);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// Output of gather with shape { W, C }</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGather_Info = outputInfo;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  outputGather_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"W"</span>], keyIndices[<span class="stringliteral">"C"</span>] });</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> arm_compute::TensorInfo aclOutputGatherInfo = BuildArmComputeTensorInfo(outputGather_Info);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">auto</span> aclAxis = <a class="code" href="namespacearmnn.xhtml#a44a3b98b37a25c995aa9e4dae7d7b456">ComputeAclAxis</a>(0, params_K_C_Info);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">auto</span> statusGather =</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  arm_compute::NEGather::validate(&aclParamsInfo, &aclFlattenedIndicesInfo, &aclOutputGatherInfo, aclAxis);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="comment"></span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="comment"> /// Validate Reshape</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="comment"></span> <span class="keyword">const</span> arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(outputInfo);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">auto</span> statusReshape = arm_compute::NEReshapeLayer::validate(&aclOutputGatherInfo, &aclOutputInfo);</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"> /// Return OK if all the layers are valid</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment"></span> <span class="keyword">auto</span> okCode = arm_compute::ErrorCode::OK;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">if</span> (statusMul.error_code() == okCode &&</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  statusReduceSum.error_code() == okCode &&</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  statusGather.error_code() == okCode &&</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  statusReshape.error_code() == okCode)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::OK,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="stringliteral">"All GatherND layers validate status OK."</span>);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a>(arm_compute::ErrorCode::RUNTIME_ERROR,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="stringliteral">"GatherND layer validate status failed."</span>);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  }</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> }</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="classarmnn_1_1_neon_gather_nd_workload.xhtml#aed7f95d9f00861351b0bd4d7b17e27b2"> 97</a></span> <a class="code" href="classarmnn_1_1_neon_gather_nd_workload.xhtml#aed7f95d9f00861351b0bd4d7b17e27b2">NeonGatherNdWorkload::NeonGatherNdWorkload</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_gather_nd_queue_descriptor.xhtml">GatherNdQueueDescriptor</a>& descriptor,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& info)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  : <a class="code" href="classarmnn_1_1_neon_base_workload.xhtml">NeonBaseWorkload</a><<a class="code" href="structarmnn_1_1_gather_nd_queue_descriptor.xhtml">GatherNdQueueDescriptor</a>>(descriptor, info)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a">ValidateInputsOutputs</a>(<span class="stringliteral">"NeonGatherNdWorkload"</span>, 2, 1);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paramsInfo = info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> indicesInfo = info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])->GetTensor();</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  arm_compute::ITensor& indices = PolymorphicDowncast<IAclTensorHandle*>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[1])->GetTensor();</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(<a class="code" href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0])->GetTensor();</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>  <span class="comment">// Calculate ND, K, W, C.</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::map<std::string, unsigned int> keyIndices = <a class="code" href="namespacearmnn.xhtml#ac40d3e4035af5fbe68d9e126a8d6367c">CalculateGatherNdKeyIndices</a>(paramsInfo, indicesInfo);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="comment"></span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment"> /// Calculate flattened indices: m_FlattenedIndices = indices * m_FlattenedCoeff.</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment"> /// This could be done using MatMul instead of multiplication followed by reduce sum operation,</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment"> /// but GeMM does not support s32 at the moment.</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">// Prepare the tensor to store the output of the reduce_sum operation</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> flattenedIndices_Info = indicesInfo;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  flattenedIndices_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"W"</span>] });</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  BuildArmComputeTensor(m_FlattenedIndices, flattenedIndices_Info);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_FlattenedIndices);</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>  <span class="comment">// Reshape indices into { W, ND }</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  indices.info()->set_tensor_shape(BuildArmComputeTensorShape({ keyIndices[<span class="stringliteral">"W"</span>], keyIndices[<span class="stringliteral">"ND"</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>  <span class="comment">// Calculate the m_FlattenedCoeff</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> paramsShape = paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::vector<int32_t> flattenedCoeff(keyIndices[<span class="stringliteral">"ND"</span>], 1);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1; i < keyIndices[<span class="stringliteral">"ND"</span>]; ++i)</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  flattenedCoeff[i - 1] = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(paramsShape[i]);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = keyIndices[<span class="stringliteral">"ND"</span>] - 1; i > 0; --i)</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>  flattenedCoeff[i - 1] *= flattenedCoeff[i];</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  }</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> flattenedCoeff_Info = indicesInfo;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  flattenedCoeff_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"ND"</span>] });</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  BuildArmComputeTensor(m_FlattenedCoeff, flattenedCoeff_Info);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_FlattenedCoeff);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(indicesInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="stringliteral">"flattenedCoeff must be same data type as m_FlattenedCoeff"</span>);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  CopyArmComputeITensorData<int32_t>(flattenedCoeff.data(), m_FlattenedCoeff);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="comment">// Prepare the tensor to store the output of the multiplication</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputMul_Info = indicesInfo;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  outputMul_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"W"</span>], keyIndices[<span class="stringliteral">"ND"</span>] });</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  BuildArmComputeTensor(m_OutputMul, outputMul_Info);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_OutputMul);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// Multiply</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  m_MulLayer.configure(&indices,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  &m_FlattenedCoeff,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  &m_OutputMul,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  1.0f,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  arm_compute::ConvertPolicy::WRAP,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  arm_compute::RoundingPolicy::TO_ZERO,</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  arm_compute::ActivationLayerInfo());</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="comment">// Reduce Sum</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> std::vector<unsigned int> armnnReduceAxes(1, 1);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="namespacearmnn.xhtml#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> coords = BuildArmComputeReductionCoordinates(m_OutputMul.info()->num_dimensions(),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  outputMul_Info.GetNumDimensions(),</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  armnnReduceAxes);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  m_ReduceSumLayer.configure(&m_OutputMul,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  &m_FlattenedIndices,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  static_cast<unsigned int>(coords[0]),</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  arm_compute::ReductionOperation::SUM,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">false</span>);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="comment"></span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="comment"> /// Call Gather with adequate shapes</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="comment"></span> <span class="comment">// Reshape params into { K, C }</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"K"</span>], keyIndices[<span class="stringliteral">"C"</span>] });</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  input.info()->set_tensor_shape(BuildArmComputeTensorShape(paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()));</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">// Reshape output to have the shape given by gather { W, C }</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// (the original outputInfo has the shape given by gatherNd)</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGather_Info = outputInfo;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  outputGather_Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ keyIndices[<span class="stringliteral">"W"</span>], keyIndices[<span class="stringliteral">"C"</span>] });</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  BuildArmComputeTensor(m_OutputGather, outputGather_Info);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_OutputGather);</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>  m_GatherLayer.configure(&input, &m_FlattenedIndices, &m_OutputGather, <a class="code" href="namespacearmnn.xhtml#a44a3b98b37a25c995aa9e4dae7d7b456">ComputeAclAxis</a>(0, paramsInfo));</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Reshape output to the original output shape</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  m_ReshapeLayer.configure(&m_OutputGather, &output);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> }</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"><a class="line" href="classarmnn_1_1_neon_gather_nd_workload.xhtml#ae071e8822437c78baea75c3aef3a263a"> 190</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_neon_gather_nd_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">NeonGatherNdWorkload::Execute</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="keyword"></span>{</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="_neon_workload_utils_8hpp.xhtml#a9165e41bcaf1b90f9ff91ef681e88c4f">ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID</a>(<span class="stringliteral">"NeonGatherNdWorkload_Execute"</span>, this-><a class="code" href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">GetGuid</a>());</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  m_MulLayer.run();</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  m_ReduceSumLayer.run();</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  m_GatherLayer.run();</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  m_ReshapeLayer.run();</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> }</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> } <span class="comment">//namespace armnn</span></div><div class="ttc" id="namespacearmnn_xhtml_a44a3b98b37a25c995aa9e4dae7d7b456"><div class="ttname"><a href="namespacearmnn.xhtml#a44a3b98b37a25c995aa9e4dae7d7b456">armnn::ComputeAclAxis</a></div><div class="ttdeci">int ComputeAclAxis(const int &armnnAxis, const armnn::TensorInfo &tensor)</div><div class="ttdoc">Function to convert ArmNN axis (left to right) to ACL axis (right to left) ranging from [-rank...</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.xhtml#l00264">ArmComputeUtils.hpp:264</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac40d3e4035af5fbe68d9e126a8d6367c"><div class="ttname"><a href="namespacearmnn.xhtml#ac40d3e4035af5fbe68d9e126a8d6367c">armnn::CalculateGatherNdKeyIndices</a></div><div class="ttdeci">std::map< std::string, unsigned int > CalculateGatherNdKeyIndices(TensorInfo inputInfo0, TensorInfo inputInfo1)</div><div class="ttdoc">Calculates the key index values needed for GatherNd: N, ND, K, W, C (N is always 1) ...</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.xhtml#l00300">WorkloadUtils.cpp:300</a></div></div>
<div class="ttc" id="_arm_compute_utils_8hpp_xhtml"><div class="ttname"><a href="_arm_compute_utils_8hpp.xhtml">ArmComputeUtils.hpp</a></div></div>
<div class="ttc" id="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_aec41b8c86e61ce02a07b8215bf8bc073"><div class="ttname"><a href="namespacearmnn.xhtml#aec41b8c86e61ce02a07b8215bf8bc073">armnn::NeonGatherNdWorkloadValidate</a></div><div class="ttdeci">arm_compute::Status NeonGatherNdWorkloadValidate(const TensorInfo &paramsInfo, const TensorInfo &indicesInfo, const TensorInfo &outputInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_gather_nd_workload_8cpp_source.xhtml#l00014">NeonGatherNdWorkload.cpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac6e86c1def7f674d3c4cb7f577874aa6"><div class="ttname"><a href="namespacearmnn.xhtml#ac6e86c1def7f674d3c4cb7f577874aa6">armnn::Coordinates</a></div><div class="ttdeci">std::array< unsigned int, MaxNumOfTensorDimensions > Coordinates</div><div class="ttdef"><b>Definition:</b> <a href="_internal_types_8hpp_source.xhtml#l00015">InternalTypes.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_aaff95a48875d8fb4a616352906660ca9"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#aaff95a48875d8fb4a616352906660ca9">armnn::BaseWorkload< GatherNdQueueDescriptor >::GetGuid</a></div><div class="ttdeci">arm::pipe::ProfilingGuid GetGuid() const final</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_8hpp_source.xhtml#l00059">Workload.hpp:59</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a765d2cee4ccce5b9467e0c2b6d25b84a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a">armnn::QueueDescriptor::ValidateInputsOutputs</a></div><div class="ttdeci">void ValidateInputsOutputs(const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00474">WorkloadData.cpp:474</a></div></div>
<div class="ttc" id="structarmnn_1_1_gather_nd_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_gather_nd_queue_descriptor.xhtml">armnn::GatherNdQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00542">WorkloadData.hpp:542</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="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00193">Tensor.hpp:193</a></div></div>
<div class="ttc" id="_polymorphic_downcast_8hpp_xhtml"><div class="ttname"><a href="_polymorphic_downcast_8hpp.xhtml">PolymorphicDowncast.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_neon_base_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_neon_base_workload.xhtml">armnn::NeonBaseWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_neon_base_workload_8hpp_source.xhtml#l00013">NeonBaseWorkload.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_ac97905bfa0daab357b91df1347600309"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">armnn::WorkloadInfo::m_InputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_InputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00018">WorkloadInfo.hpp:18</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_workload_xhtml_afb8d2c8817c75de9d01a4c0e0d5c160b"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml#afb8d2c8817c75de9d01a4c0e0d5c160b">armnn::BaseWorkload< GatherNdQueueDescriptor >::m_Data</a></div><div class="ttdeci">GatherNdQueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_8hpp_source.xhtml#l00081">Workload.hpp:81</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00198">Tensor.hpp:198</a></div></div>
<div class="ttc" id="classarmnn_1_1_neon_gather_nd_workload_xhtml_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_neon_gather_nd_workload.xhtml#ae071e8822437c78baea75c3aef3a263a">armnn::NeonGatherNdWorkload::Execute</a></div><div class="ttdeci">virtual void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_neon_gather_nd_workload_8cpp_source.xhtml#l00190">NeonGatherNdWorkload.cpp:190</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70b"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">armnn::Status</a></div><div class="ttdeci">Status</div><div class="ttdoc">enumeration </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00042">Types.hpp:42</a></div></div>
<div class="ttc" id="_neon_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_neon_workload_utils_8hpp.xhtml">NeonWorkloadUtils.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_neon_gather_nd_workload_xhtml_aed7f95d9f00861351b0bd4d7b17e27b2"><div class="ttname"><a href="classarmnn_1_1_neon_gather_nd_workload.xhtml#aed7f95d9f00861351b0bd4d7b17e27b2">armnn::NeonGatherNdWorkload::NeonGatherNdWorkload</a></div><div class="ttdeci">NeonGatherNdWorkload(const GatherNdQueueDescriptor &descriptor, const WorkloadInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_gather_nd_workload_8cpp_source.xhtml#l00097">NeonGatherNdWorkload.cpp:97</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_a67b178f8a836bc1e52b8de109760adfd"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">armnn::WorkloadInfo::m_OutputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_OutputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00019">WorkloadInfo.hpp:19</a></div></div>
<div class="ttc" id="_neon_gather_nd_workload_8hpp_xhtml"><div class="ttname"><a href="_neon_gather_nd_workload_8hpp.xhtml">NeonGatherNdWorkload.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00027">WorkloadData.hpp:27</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00026">WorkloadData.hpp:26</a></div></div>
<div class="ttc" id="_neon_workload_utils_8hpp_xhtml_a9165e41bcaf1b90f9ff91ef681e88c4f"><div class="ttname"><a href="_neon_workload_utils_8hpp.xhtml#a9165e41bcaf1b90f9ff91ef681e88c4f">ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID(name, guid)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_workload_utils_8hpp_source.xhtml#l00024">NeonWorkloadUtils.hpp:24</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00195">Tensor.hpp:195</a></div></div>
<div class="ttc" id="_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_utils_8hpp.xhtml">WorkloadUtils.hpp</a></div></div>
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