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| <div class="title">RefL2NormalizationWorkload.cpp</div> </div> |
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| <a href="_ref_l2_normalization_workload_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> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_ref_l2_normalization_workload_8hpp.html">RefL2NormalizationWorkload.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_ref_workload_utils_8hpp.html">RefWorkloadUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_decoders_8hpp.html">Decoders.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "<a class="code" href="_encoders_8hpp.html">Encoders.hpp</a>"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_profiling_8hpp.html">Profiling.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.html">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.html">armnnUtils</a>;</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> {</div><div class="line"><a name="l00023"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_l2_normalization_workload.html#a722973639581b632387fc8d36f106aca"> 23</a></span> <a class="code" href="classarmnn_1_1_ref_l2_normalization_workload.html#a722973639581b632387fc8d36f106aca">RefL2NormalizationWorkload::RefL2NormalizationWorkload</a>(</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_l2_normalization_queue_descriptor.html">L2NormalizationQueueDescriptor</a>& descriptor,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.html">WorkloadInfo</a>& info)</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  : <a class="code" href="classarmnn_1_1_base_workload.html">BaseWorkload</a><<a class="code" href="structarmnn_1_1_l2_normalization_queue_descriptor.html">L2NormalizationQueueDescriptor</a>>(descriptor, info) {}</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="classarmnn_1_1_ref_l2_normalization_workload.html#ae071e8822437c78baea75c3aef3a263a"> 28</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_ref_l2_normalization_workload.html#ae071e8822437c78baea75c3aef3a263a">RefL2NormalizationWorkload::Execute</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="keyword"></span>{</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <a class="code" href="_profiling_8hpp.html#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.html#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>, <span class="stringliteral">"RefL2NormalizationWorkload_Execute"</span>);</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>& inputInfo = <a class="code" href="namespacearmnn.html#a93d269806f34407695dc10f510001c30">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0]);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>& outputInfo = <a class="code" href="namespacearmnn.html#a93d269806f34407695dc10f510001c30">GetTensorInfo</a>(<a class="code" href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]);</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> inputDecoder = MakeDecoder<float>(inputInfo, <a class="code" href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0]->Map());</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keyword">auto</span> outputEncoder = MakeEncoder<float>(outputInfo, <a class="code" href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]->Map());</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html">DataLayoutIndexed</a> dataLayout(<a class="code" href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& shape = inputInfo.GetShape();</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShapeArray[4];</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idxShift = 4 - boost::numeric_cast<<span class="keywordtype">int</span>>(shape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = (idxShift == 0) ? shape[0] : 1;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  paddedShapeArray[0] = batches;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channelsIdx = boost::numeric_cast<<span class="keywordtype">int</span>>(dataLayout.GetChannelsIndex());</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = (channelsIdx - idxShift >= 0)</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  ? shape[boost::numeric_cast<unsigned int>(channelsIdx - idxShift)]</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  : 1;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  paddedShapeArray[channelsIdx] = channels;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> heightIdx = boost::numeric_cast<<span class="keywordtype">int</span>>(dataLayout.GetHeightIndex());</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = (heightIdx - idxShift >= 0)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  ? shape[boost::numeric_cast<unsigned int>(heightIdx - idxShift)]</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  : 1;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  paddedShapeArray[heightIdx] = height;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> widthIdx = boost::numeric_cast<<span class="keywordtype">int</span>>(dataLayout.GetWidthIndex());</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = (widthIdx - idxShift >= 0)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  ? shape[boost::numeric_cast<unsigned int>(widthIdx - idxShift)]</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  : 1;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  paddedShapeArray[widthIdx] = width;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>& paddedShape = <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a>(4, paddedShapeArray);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n = 0; n < batches; ++n)</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> c = 0; c < channels; ++c)</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> h = 0; h < height; ++h)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < width; ++w)</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  {</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordtype">float</span> reduction = 0.0;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 0; d < channels; ++d)</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIndex = dataLayout.GetIndex(paddedShape, n, d, h, w);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  (*inputDecoder)[inputIndex];</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> value = inputDecoder->Get();</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  reduction += value * value;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = dataLayout.GetIndex(paddedShape, n, c, h, w);</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="keywordtype">float</span> <a class="code" href="structarmnn_1_1maximum.html">maximum</a> = reduction < <a class="code" href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.html#a11c821c7524251004a72ed13c510853c">m_Eps</a> ? <a class="code" href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">m_Data</a>.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.html#a11c821c7524251004a72ed13c510853c">m_Eps</a> : reduction;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> scale = 1.0f / sqrtf(maximum);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  (*inputDecoder)[index];</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  (*outputEncoder)[index];</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  outputEncoder->Set(inputDecoder->Get() * scale);</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"> 97</span>  }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> } <span class="comment">//namespace armnn</span></div><div class="ttc" id="_profiling_8hpp_html_a5ccc65e2c464ac05ce311fdae7ede03a"><div class="ttname"><a href="_profiling_8hpp.html#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT(backendId, name)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.html#l00170">Profiling.hpp:170</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="structarmnn_1_1_queue_descriptor_with_parameters_html_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00049">WorkloadData.hpp:49</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> |
| <div class="ttc" id="_ref_l2_normalization_workload_8hpp_html"><div class="ttname"><a href="_ref_l2_normalization_workload_8hpp.html">RefL2NormalizationWorkload.hpp</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_workload_info_html"><div class="ttname"><a href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.html#l00016">WorkloadInfo.hpp:16</a></div></div> |
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| <div class="ttc" id="namespacearmnn_utils_html"><div class="ttname"><a href="namespacearmnn_utils.html">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.html#l00013">DataLayoutIndexed.hpp:13</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_html_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.html#a6089e1ca91914015777ea780a513131a">armnn::L2NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00606">Descriptors.hpp:606</a></div></div> |
| <div class="ttc" id="classarmnn_1_1_base_workload_html_a0a487c549c63319505095b855ea3c195"><div class="ttname"><a href="classarmnn_1_1_base_workload.html#a0a487c549c63319505095b855ea3c195">armnn::BaseWorkload< L2NormalizationQueueDescriptor >::m_Data</a></div><div class="ttdeci">const L2NormalizationQueueDescriptor m_Data</div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.html#l00046">Workload.hpp:46</a></div></div> |
| <div class="ttc" id="classarmnn_1_1_ref_l2_normalization_workload_html_ae071e8822437c78baea75c3aef3a263a"><div class="ttname"><a href="classarmnn_1_1_ref_l2_normalization_workload.html#ae071e8822437c78baea75c3aef3a263a">armnn::RefL2NormalizationWorkload::Execute</a></div><div class="ttdeci">void Execute() const override</div><div class="ttdef"><b>Definition:</b> <a href="_ref_l2_normalization_workload_8cpp_source.html#l00028">RefL2NormalizationWorkload.cpp:28</a></div></div> |
| <div class="ttc" id="structarmnn_1_1maximum_html"><div class="ttname"><a href="structarmnn_1_1maximum.html">armnn::maximum</a></div><div class="ttdef"><b>Definition:</b> <a href="_maximum_8hpp_source.html#l00013">Maximum.hpp:13</a></div></div> |
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| <div class="ttc" id="_decoders_8hpp_html"><div class="ttname"><a href="_decoders_8hpp.html">Decoders.hpp</a></div></div> |
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| <div class="ttc" id="namespacearmnn_html_ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64"><div class="ttname"><a href="namespacearmnn.html#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a></div><div class="ttdoc">CPU Execution: Reference C++ kernels. </div></div> |
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| <div class="ttc" id="structarmnn_1_1_queue_descriptor_html_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00031">WorkloadData.hpp:31</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_queue_descriptor_html_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00030">WorkloadData.hpp:30</a></div></div> |
| <div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_html"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html">armnnUtils::DataLayoutIndexed</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.html#l00017">DataLayoutIndexed.hpp:17</a></div></div> |
| <div class="ttc" id="classarmnn_1_1_base_workload_html"><div class="ttname"><a href="classarmnn_1_1_base_workload.html">armnn::BaseWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_8hpp_source.html#l00028">Workload.hpp:28</a></div></div> |
| <div class="ttc" id="namespacearmnn_html_a93d269806f34407695dc10f510001c30"><div class="ttname"><a href="namespacearmnn.html#a93d269806f34407695dc10f510001c30">armnn::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)</div><div class="ttdoc">float32 helpers </div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.html#l00025">RefWorkloadUtils.hpp:25</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_html_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.html#a11c821c7524251004a72ed13c510853c">armnn::L2NormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00604">Descriptors.hpp:604</a></div></div> |
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