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<a href="backends_2reference_2workloads_2_stack_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="_stack_8hpp.html">Stack.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> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></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> </div><div class="line"><a name="l00012"></a><span class="lineno"><a class="line" href="namespacearmnn.html#a6ef2dcac2ec0683d52df1b051404e7d6"> 12</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#a6ef2dcac2ec0683d52df1b051404e7d6">Stack</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_stack_queue_descriptor.html">StackQueueDescriptor</a>& data,</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  std::vector<std::unique_ptr<<a class="code" href="classarmnn_1_1_decoder.html">Decoder<float></a>>>& inputs,</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  <a class="code" href="classarmnn_1_1_encoder.html">Encoder<float></a>& output)</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> {</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="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>(data.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]);</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</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>(data.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0]);</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNumDims = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNumDims = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>& outputDims = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>& inputDims = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> axis = data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_stack_descriptor.html#ab218de7805899c8412d75d1fd1d846d2">m_Axis</a>;</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">// Initialise output data</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputElements = 1;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i<outputNumDims; ++i)</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  {</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  numOutputElements *= outputDims[i];</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  }</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iNumTensors = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(data.<a class="code" href="structarmnn_1_1_queue_descriptor.html#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size());</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iBatchSize = inputDims[0];</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iChannels = (inputNumDims > 1) ? inputDims[1] : 1;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iHeight = (inputNumDims > 2) ? inputDims[2] : 1;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iWidth = (inputNumDims > 3) ? inputDims[3] : 1;</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> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> oBatchSize = outputDims[1];</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> oChannels = (outputNumDims > 2) ? outputDims[2] : 1;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> oHeight = (outputNumDims > 3) ? outputDims[3] : 1;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> oWidth = (outputNumDims > 4) ? outputDims[4] : 1;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="comment">// Array to store the input coordinates</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// iCoordinates[0] = i, iCoordinates[1] = bi, iCoordinates[2] = ci</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// iCoordinates[3] = hi, iCoordinates[4] = wi, iCoordinates[5] = 0</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// iCoordinates[5] will be always zero and used for not incrementing</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// the output when the input has less than 4 dimensions</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  std::array<unsigned int, 6> iCoordinates{ 0 };</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// Array of pointers used to map the output coordinates to the input ones, in accordance with the axis</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// This array is initialized with &iCoordinates[5] since this will be always zero</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  std::array<unsigned int *, 5> oCoordinates = { &iCoordinates[5],</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  &iCoordinates[5],</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  &iCoordinates[5],</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  &iCoordinates[5],</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  &iCoordinates[5] };</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="comment">// Set the axis coordinate</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  oCoordinates[axis] = &iCoordinates[0];</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">// Map the output coordinates, accounting for the axis</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim_shift = 0;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim = 0; dim < inputNumDims; ++dim)</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">if</span>(dim == axis)</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>  dim_shift++;</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>  oCoordinates[dim + dim_shift] = &iCoordinates[dim + 1];</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> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// Alias for the input coordinates</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &i = iCoordinates[0];</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &bi = iCoordinates[1];</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &ci = iCoordinates[2];</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &hi = iCoordinates[3];</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &wi = iCoordinates[4];</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// Alias for the output coordinates</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &o = *(oCoordinates[0]);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &bo = *(oCoordinates[1]);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &co = *(oCoordinates[2]);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &ho = *(oCoordinates[3]);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &wo = *(oCoordinates[4]);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">// Stack tensors</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">for</span>(; i < iNumTensors; ++(i))</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>  <span class="keywordflow">for</span>(bi = 0; bi < iBatchSize; ++(bi))</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">for</span>(ci = 0; ci < iChannels; ++(ci))</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>  <span class="keywordflow">for</span>(hi = 0; hi < iHeight; ++(hi))</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">for</span>(wi = 0; wi < iWidth; ++(wi))</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>  output[o * oWidth * oHeight * oChannels * oBatchSize +</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  bo * oWidth * oHeight * oChannels +</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  co * oWidth * oHeight +</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  ho * oWidth +</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  wo];</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  output.<a class="code" href="classarmnn_1_1_encoder.html#ae3b62b846a9c239f332830b9e36030eb">Set</a>(inputs[i]->Get());</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>  ++(*(inputs[i]));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  }</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  }</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> } <span class="comment">// namespace armnn</span></div><div class="ttc" id="structarmnn_1_1_stack_descriptor_html_ab218de7805899c8412d75d1fd1d846d2"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.html#ab218de7805899c8412d75d1fd1d846d2">armnn::StackDescriptor::m_Axis</a></div><div class="ttdeci">uint32_t m_Axis</div><div class="ttdoc">0-based axis along which to stack the input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00972">Descriptors.hpp:972</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00092">Tensor.hpp:92</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="classarmnn_1_1_decoder_html"><div class="ttname"><a href="classarmnn_1_1_decoder.html">armnn::Decoder< float ></a></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="namespacearmnn_html_a6ef2dcac2ec0683d52df1b051404e7d6"><div class="ttname"><a href="namespacearmnn.html#a6ef2dcac2ec0683d52df1b051404e7d6">armnn::Stack</a></div><div class="ttdeci">void Stack(const StackQueueDescriptor &data, std::vector< std::unique_ptr< Decoder< float >>> &inputs, Encoder< float > &output)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_stack_8cpp_source.html#l00012">Stack.cpp:12</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00088">Tensor.hpp:88</a></div></div>
<div class="ttc" id="classarmnn_1_1_encoder_html_ae3b62b846a9c239f332830b9e36030eb"><div class="ttname"><a href="classarmnn_1_1_encoder.html#ae3b62b846a9c239f332830b9e36030eb">armnn::Encoder::Set</a></div><div class="ttdeci">virtual void Set(IType right)=0</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="_ref_workload_utils_8hpp_html"><div class="ttname"><a href="_ref_workload_utils_8hpp.html">RefWorkloadUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_stack_queue_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_stack_queue_descriptor.html">armnn::StackQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00124">WorkloadData.hpp:124</a></div></div>
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