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| author | David Monahan <david.monahan@arm.com> | 2023-03-22 16:48:58 +0000 |
|---|---|---|
| committer | David Monahan <david.monahan@arm.com> | 2023-03-22 16:48:58 +0000 |
| commit | ae050524109f1ce827962665436ef7430f2ac479 (patch) | |
| tree | a087fe0c77570971dd7979f2757426c24e91afc7 /23.02/_transpose_convolution2d_8cpp_source.xhtml | |
| parent | 8d2ca734165a068478df7cffa46185680b05cd20 (diff) | |
| download | armnn-ae050524109f1ce827962665436ef7430f2ac479.tar.gz | |
IVGCVSW-7255 Update Doxygen Documentation and publish on GitHub.
* Updating Doxygen documentation for 23.02 release.
Signed-off-by: David Monahan <david.monahan@arm.com>
Change-Id: I545574ff7664b4595d2fe6a91a3c35d2ad55df82
Diffstat (limited to '23.02/_transpose_convolution2d_8cpp_source.xhtml')
| -rw-r--r-- | 23.02/_transpose_convolution2d_8cpp_source.xhtml | 236 |
1 files changed, 200 insertions, 36 deletions
diff --git a/23.02/_transpose_convolution2d_8cpp_source.xhtml b/23.02/_transpose_convolution2d_8cpp_source.xhtml index c8953d6304..96fffcea3c 100644 --- a/23.02/_transpose_convolution2d_8cpp_source.xhtml +++ b/23.02/_transpose_convolution2d_8cpp_source.xhtml @@ -8,7 +8,7 @@ <head> <meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> <meta http-equiv="X-UA-Compatible" content="IE=9"/> -<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="generator" content="Doxygen 1.8.17"/> <meta name="robots" content="NOINDEX, NOFOLLOW" /> <meta name="viewport" content="width=device-width, initial-scale=1"/> <title>ArmNN: src/backends/reference/workloads/TransposeConvolution2d.cpp Source File</title> @@ -19,9 +19,6 @@ <script type="text/javascript" src="resize.js"></script> <script type="text/javascript" src="navtreedata.js"></script> <script type="text/javascript" src="navtree.js"></script> -<script type="text/javascript"> - $(document).ready(initResizable); -</script> <link href="search/search.css" rel="stylesheet" type="text/css"/> <script type="text/javascript" src="search/searchdata.js"></script> <script type="text/javascript" src="search/search.js"></script> @@ -30,7 +27,8 @@ extensions: ["tex2jax.js"], jax: ["input/TeX","output/HTML-CSS"], }); -</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +</script> +<script type="text/javascript" async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> <link href="doxygen.css" rel="stylesheet" type="text/css" /> <link href="stylesheet.css" rel="stylesheet" type="text/css"/> </head> @@ -51,18 +49,21 @@ </table> </div> <!-- end header part --> -<!-- Generated by Doxygen 1.8.13 --> +<!-- Generated by Doxygen 1.8.17 --> <script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ var searchBox = new SearchBox("searchBox", "search",false,'Search'); +/* @license-end */ </script> <script type="text/javascript" src="menudata.js"></script> <script type="text/javascript" src="menu.js"></script> <script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ $(function() { initMenu('',true,false,'search.php','Search'); $(document).ready(function() { init_search(); }); }); -</script> +/* @license-end */</script> <div id="main-nav"></div> </div><!-- top --> <div id="side-nav" class="ui-resizable side-nav-resizable"> @@ -76,7 +77,9 @@ $(function() { </div> </div> <script type="text/javascript"> -$(document).ready(function(){initNavTree('_transpose_convolution2d_8cpp_source.xhtml','');}); +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ +$(document).ready(function(){initNavTree('_transpose_convolution2d_8cpp_source.xhtml',''); initResizable(); }); +/* @license-end */ </script> <div id="doc-content"> <!-- window showing the filter options --> @@ -98,41 +101,202 @@ $(document).ready(function(){initNavTree('_transpose_convolution2d_8cpp_source.x <div class="title">TransposeConvolution2d.cpp</div> </div> </div><!--header--> <div class="contents"> -<a href="_transpose_convolution2d_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 © 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="_transpose_convolution2d_8hpp.xhtml">TransposeConvolution2d.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></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> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#affec174d91f234497dfbceba5e251dee"> 15</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#affec174d91f234497dfbceba5e251dee">TransposeConvolution2dImpl</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a>& descriptor,</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_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& inputDecoder,</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape,</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <a class="code" href="classarmnn_1_1_encoder.xhtml">Encoder<float></a>& outputEncoder,</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& weightsShape,</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& weightsDecoder,</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>* biasesDecoder)</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> && !biasesDecoder)</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  {</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Biases enabled but no bias data provided"</span>);</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  }</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dataLayoutIndexed(descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelsIndex = dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> heightIndex = dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> widthIndex = dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = inputShape[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">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputShape[widthIndex];</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> inputHeight = inputShape[heightIndex];</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> inputDepth = inputShape[channelsIndex];</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsHeight = weightsShape[heightIndex];</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> weightsWidth = weightsShape[widthIndex];</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> weightsDepth = weightsShape[channelsIndex];</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </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> outputHeight = outputShape[heightIndex];</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> outputWidth = outputShape[widthIndex];</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDepth = outputShape[channelsIndex];</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">unsigned</span> <span class="keywordtype">int</span> paddingLeft = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>;</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> paddingTop = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</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>  std::vector<float> outputBuffer(outputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">const</span> std::vector<float> inputVec = inputDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">DecodeTensor</a>(inputShape);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">const</span> std::vector<float> filterVec = weightsDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">DecodeTensor</a>(weightsShape);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch = 0u; batch < numBatches; ++batch)</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yInput = 0u; yInput < inputHeight; ++yInput)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xInput = 0u; xInput < inputWidth; ++xInput)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xOutputOrigin = xInput * strideX - paddingLeft;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yOutputOrigin = yInput * strideY - paddingTop;</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> dOutput = 0u; dOutput < outputDepth; ++dOutput)</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> yWeights = 0u; yWeights < weightsHeight; ++yWeights)</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> xWeights = 0u; xWeights < weightsWidth; ++xWeights)</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> yOutput = yOutputOrigin + yWeights;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xOutput = xOutputOrigin + xWeights;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span> (yOutput < outputHeight && xOutput< outputWidth)</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dInput = 0u; dInput < inputDepth; dInput++)</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIndex;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIndex;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsIndex;</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>  <span class="keywordflow">if</span>(descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  {</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  inputIndex = batch * inputHeight * inputWidth * inputDepth +</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  yInput * inputWidth * inputDepth +</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  xInput * inputDepth +</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  dInput;</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>  weightsIndex = dOutput * weightsHeight * weightsWidth * weightsDepth +</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  yWeights * weightsWidth * weightsDepth +</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  xWeights * weightsDepth +</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  dInput;</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>  outputIndex = batch * outputHeight * outputWidth * outputDepth +</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  yOutput * outputWidth * outputDepth +</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  xOutput * outputDepth +</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  dOutput;</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>  <span class="keywordflow">else</span></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>  inputIndex = batch * inputDepth * inputHeight * inputWidth +</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  dInput * inputHeight * inputWidth +</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  yInput * inputWidth +</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  xInput;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  weightsIndex = dOutput * weightsDepth * weightsHeight * weightsWidth +</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  dInput * weightsHeight * weightsWidth +</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  yWeights * weightsWidth +</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  xWeights;</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>  outputIndex = batch * outputDepth * outputHeight * outputWidth +</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  dOutput * outputHeight * outputWidth +</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  yOutput * outputWidth +</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  xOutput;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  outputBuffer[outputIndex] += inputVec[inputIndex] * filterVec[weightsIndex];</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</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> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</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>  }</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  }</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// Apply bias (if enabled)</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  outputEncoder[0];</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& rBiasesDecoder = *biasesDecoder;</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch = 0u; batch < numBatches; ++batch)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dOutput = 0u; dOutput < outputDepth; ++dOutput)</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  rBiasesDecoder[dOutput];</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yOutput = 0u; yOutput < outputHeight; ++yOutput)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xOutput = 0u; xOutput < outputWidth; ++xOutput)</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  {</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIndex =</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a1e25d8623da985a43597b5756c73b206">GetIndex</a>(outputShape, batch, dOutput, yOutput, xOutput);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  outputBuffer[outputIndex] += rBiasesDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#ac729108381e2340bea12877971713ecb">Get</a>();</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  }</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</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>  }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  outputEncoder[0];</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordflow">for</span> (<span class="keywordtype">float</span> output : outputBuffer)</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  {</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  outputEncoder.<a class="code" href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">Set</a>(output);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  ++outputEncoder;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> }</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">// namespace armnn</span></div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorShape::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdoc">Function that calculates the tensor elements by multiplying all dimension size which are Specified...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00181">Tensor.cpp:181</a></div></div> -<div class="ttc" id="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</a></div></div> -<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00025">DataLayoutIndexed.hpp:25</a></div></div> -<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01387">Descriptors.hpp:1387</a></div></div> -<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01428">Descriptors.hpp:1428</a></div></div> -<div class="ttc" id="classarmnn_1_1_decoder_xhtml_aafe0168dd5ece89e7c62e8d83a4e57cd"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">armnn::Decoder::DecodeTensor</a></div><div class="ttdeci">virtual std::vector< float > DecodeTensor(const TensorShape &tensorShape, bool isDepthwise=false)=0</div></div> -<div class="ttc" id="namespacearmnn_xhtml_affec174d91f234497dfbceba5e251dee"><div class="ttname"><a href="namespacearmnn.xhtml#affec174d91f234497dfbceba5e251dee">armnn::TransposeConvolution2dImpl</a></div><div class="ttdeci">void TransposeConvolution2dImpl(const TransposeConvolution2dDescriptor &descriptor, const TensorShape &inputShape, Decoder< float > &inputDecoder, const TensorShape &outputShape, Encoder< float > &outputEncoder, const TensorShape &weightsShape, Decoder< float > &weightsDecoder, Decoder< float > *biasesDecoder)</div><div class="ttdef"><b>Definition:</b> <a href="_transpose_convolution2d_8cpp_source.xhtml#l00015">TransposeConvolution2d.cpp:15</a></div></div> -<div class="ttc" id="classarmnn_1_1_encoder_xhtml_ae3b62b846a9c239f332830b9e36030eb"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">armnn::Encoder::Set</a></div><div class="ttdeci">virtual void Set(IType right)=0</div></div> -<div class="ttc" id="classarmnn_1_1_encoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml">armnn::Encoder< float ></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_utils_1_1_data_layout_indexed_xhtml_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00024">DataLayoutIndexed.hpp:24</a></div></div> -<div class="ttc" id="_transpose_convolution2d_8hpp_xhtml"><div class="ttname"><a href="_transpose_convolution2d_8hpp.xhtml">TransposeConvolution2d.hpp</a></div></div> -<div class="ttc" id="classarmnn_1_1_decoder_xhtml_ac729108381e2340bea12877971713ecb"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml#ac729108381e2340bea12877971713ecb">armnn::Decoder::Get</a></div><div class="ttdeci">virtual IType Get() const =0</div></div> -<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div> -<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a1e25d8623da985a43597b5756c73b206"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a1e25d8623da985a43597b5756c73b206">armnnUtils::DataLayoutIndexed::GetIndex</a></div><div class="ttdeci">unsigned int GetIndex(const armnn::TensorShape &shape, unsigned int batchIndex, unsigned int channelIndex, unsigned int heightIndex, unsigned int widthIndex) const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00028">DataLayoutIndexed.hpp:28</a></div></div> -<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::TransposeConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01420">Descriptors.hpp:1420</a></div></div> -<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::TransposeConvolution2dDescriptor::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.xhtml#l01430">Descriptors.hpp:1430</a></div></div> -<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div> -<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01416">Descriptors.hpp:1416</a></div></div> -<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01424">Descriptors.hpp:1424</a></div></div> -<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01426">Descriptors.hpp:1426</a></div></div> -<div class="ttc" id="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_compatible_types_8hpp_source.xhtml#l00010">CompatibleTypes.hpp:10</a></div></div> -<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">armnnUtils::DataLayoutIndexed::GetChannelsIndex</a></div><div class="ttdeci">unsigned int GetChannelsIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00023">DataLayoutIndexed.hpp:23</a></div></div> -<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> -<div class="ttc" id="classarmnn_1_1_decoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml">armnn::Decoder< float ></a></div></div> +<a href="_transpose_convolution2d_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 © 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="_transpose_convolution2d_8hpp.xhtml">TransposeConvolution2d.hpp</a>"</span></div> +<div class="line"><a name="l00007"></a><span class="lineno"> 7</span>  </div> +<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div> +<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  </div> +<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></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>  </div> +<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div> +<div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  </div> +<div class="line"><a name="l00015"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#affec174d91f234497dfbceba5e251dee"> 15</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#affec174d91f234497dfbceba5e251dee">TransposeConvolution2dImpl</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a>& descriptor,</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_shape.xhtml">TensorShape</a>& inputShape,</div> +<div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& inputDecoder,</div> +<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape,</div> +<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <a class="code" href="classarmnn_1_1_encoder.xhtml">Encoder<float></a>& outputEncoder,</div> +<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& weightsShape,</div> +<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& weightsDecoder,</div> +<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>* biasesDecoder)</div> +<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div> +<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> && !biasesDecoder)</div> +<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  {</div> +<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Biases enabled but no bias data provided"</span>);</div> +<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  }</div> +<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dataLayoutIndexed(descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div> +<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelsIndex = dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div> +<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> heightIndex = dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div> +<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> widthIndex = dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = inputShape[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">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputShape[widthIndex];</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> inputHeight = inputShape[heightIndex];</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> inputDepth = inputShape[channelsIndex];</div> +<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  </div> +<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsHeight = weightsShape[heightIndex];</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> weightsWidth = weightsShape[widthIndex];</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> weightsDepth = weightsShape[channelsIndex];</div> +<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  </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> outputHeight = outputShape[heightIndex];</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> outputWidth = outputShape[widthIndex];</div> +<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDepth = outputShape[channelsIndex];</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">unsigned</span> <span class="keywordtype">int</span> paddingLeft = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>;</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> paddingTop = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>;</div> +<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  </div> +<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div> +<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</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>  std::vector<float> outputBuffer(outputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), 0);</div> +<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  </div> +<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">const</span> std::vector<float> inputVec = inputDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">DecodeTensor</a>(inputShape);</div> +<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">const</span> std::vector<float> filterVec = weightsDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">DecodeTensor</a>(weightsShape);</div> +<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  </div> +<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch = 0u; batch < numBatches; ++batch)</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yInput = 0u; yInput < inputHeight; ++yInput)</div> +<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div> +<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xInput = 0u; xInput < inputWidth; ++xInput)</div> +<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div> +<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xOutputOrigin = xInput * strideX - paddingLeft;</div> +<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yOutputOrigin = yInput * strideY - paddingTop;</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> dOutput = 0u; dOutput < outputDepth; ++dOutput)</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> yWeights = 0u; yWeights < weightsHeight; ++yWeights)</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> xWeights = 0u; xWeights < weightsWidth; ++xWeights)</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> yOutput = yOutputOrigin + yWeights;</div> +<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xOutput = xOutputOrigin + xWeights;</div> +<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  </div> +<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span> (yOutput < outputHeight && xOutput< outputWidth)</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dInput = 0u; dInput < inputDepth; dInput++)</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIndex;</div> +<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIndex;</div> +<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsIndex;</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>  <span class="keywordflow">if</span>(descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div> +<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  {</div> +<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  inputIndex = batch * inputHeight * inputWidth * inputDepth +</div> +<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  yInput * inputWidth * inputDepth +</div> +<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  xInput * inputDepth +</div> +<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  dInput;</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>  weightsIndex = dOutput * weightsHeight * weightsWidth * weightsDepth +</div> +<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  yWeights * weightsWidth * weightsDepth +</div> +<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  xWeights * weightsDepth +</div> +<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  dInput;</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>  outputIndex = batch * outputHeight * outputWidth * outputDepth +</div> +<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  yOutput * outputWidth * outputDepth +</div> +<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  xOutput * outputDepth +</div> +<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  dOutput;</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>  <span class="keywordflow">else</span></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>  inputIndex = batch * inputDepth * inputHeight * inputWidth +</div> +<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  dInput * inputHeight * inputWidth +</div> +<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  yInput * inputWidth +</div> +<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  xInput;</div> +<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  </div> +<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  weightsIndex = dOutput * weightsDepth * weightsHeight * weightsWidth +</div> +<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  dInput * weightsHeight * weightsWidth +</div> +<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  yWeights * weightsWidth +</div> +<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  xWeights;</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>  outputIndex = batch * outputDepth * outputHeight * outputWidth +</div> +<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  dOutput * outputHeight * outputWidth +</div> +<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  yOutput * outputWidth +</div> +<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  xOutput;</div> +<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div> +<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  </div> +<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  outputBuffer[outputIndex] += inputVec[inputIndex] * filterVec[weightsIndex];</div> +<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div> +<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div> +<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</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>  </div> +<div class="line"><a name="l00125"></a><span class="lineno"> 125</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>  }</div> +<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  }</div> +<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  </div> +<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// Apply bias (if enabled)</span></div> +<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div> +<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div> +<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  outputEncoder[0];</div> +<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& rBiasesDecoder = *biasesDecoder;</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch = 0u; batch < numBatches; ++batch)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dOutput = 0u; dOutput < outputDepth; ++dOutput)</div> +<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div> +<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  rBiasesDecoder[dOutput];</div> +<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yOutput = 0u; yOutput < outputHeight; ++yOutput)</div> +<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  {</div> +<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> xOutput = 0u; xOutput < outputWidth; ++xOutput)</div> +<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  {</div> +<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIndex =</div> +<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a1e25d8623da985a43597b5756c73b206">GetIndex</a>(outputShape, batch, dOutput, yOutput, xOutput);</div> +<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  outputBuffer[outputIndex] += rBiasesDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#ac729108381e2340bea12877971713ecb">Get</a>();</div> +<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  }</div> +<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div> +<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</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>  }</div> +<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  outputEncoder[0];</div> +<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordflow">for</span> (<span class="keywordtype">float</span> output : outputBuffer)</div> +<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  {</div> +<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  outputEncoder.<a class="code" href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">Set</a>(output);</div> +<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  ++outputEncoder;</div> +<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  }</div> +<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> }</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">// namespace armnn</span></div> </div><!-- fragment --></div><!-- contents --> </div><!-- doc-content --> +<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_xhtml_a1e25d8623da985a43597b5756c73b206"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a1e25d8623da985a43597b5756c73b206">armnnUtils::DataLayoutIndexed::GetIndex</a></div><div class="ttdeci">unsigned int GetIndex(const armnn::TensorShape &shape, unsigned int batchIndex, unsigned int channelIndex, unsigned int heightIndex, unsigned int widthIndex) const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00028">DataLayoutIndexed.hpp:28</a></div></div> +<div class="ttc" id="a_transpose_convolution2d_8hpp_xhtml"><div class="ttname"><a href="_transpose_convolution2d_8hpp.xhtml">TransposeConvolution2d.hpp</a></div></div> +<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01416">Descriptors.hpp:1416</a></div></div> +<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::TransposeConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01420">Descriptors.hpp:1420</a></div></div> +<div class="ttc" id="a_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</a></div></div> +<div class="ttc" id="aclassarmnn_1_1_tensor_shape_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorShape::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdoc">Function that calculates the tensor elements by multiplying all dimension size which are Specified.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00181">Tensor.cpp:181</a></div></div> +<div class="ttc" id="aclassarmnn_1_1_encoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml">armnn::Encoder< float ></a></div></div> +<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01428">Descriptors.hpp:1428</a></div></div> +<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_xhtml_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00025">DataLayoutIndexed.hpp:25</a></div></div> +<div class="ttc" id="anamespacearmnn_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="aclassarmnn_utils_1_1_data_layout_indexed_xhtml_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">armnnUtils::DataLayoutIndexed::GetChannelsIndex</a></div><div class="ttdeci">unsigned int GetChannelsIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00023">DataLayoutIndexed.hpp:23</a></div></div> +<div class="ttc" id="anamespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_compatible_types_8hpp_source.xhtml#l00010">CompatibleTypes.hpp:10</a></div></div> +<div class="ttc" id="aclassarmnn_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="aclassarmnn_1_1_decoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml">armnn::Decoder< float ></a></div></div> +<div class="ttc" id="anamespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div><div class="ttdeci">@ NHWC</div></div> +<div class="ttc" id="aclassarmnn_1_1_encoder_xhtml_ae3b62b846a9c239f332830b9e36030eb"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">armnn::Encoder::Set</a></div><div class="ttdeci">virtual void Set(IType right)=0</div></div> +<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::TransposeConvolution2dDescriptor::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.xhtml#l01430">Descriptors.hpp:1430</a></div></div> +<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01387">Descriptors.hpp:1387</a></div></div> +<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout.</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div> +<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_xhtml_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00024">DataLayoutIndexed.hpp:24</a></div></div> +<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01424">Descriptors.hpp:1424</a></div></div> +<div class="ttc" id="aclassarmnn_1_1_decoder_xhtml_ac729108381e2340bea12877971713ecb"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml#ac729108381e2340bea12877971713ecb">armnn::Decoder::Get</a></div><div class="ttdeci">virtual IType Get() const =0</div></div> +<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01426">Descriptors.hpp:1426</a></div></div> +<div class="ttc" id="anamespacearmnn_xhtml_affec174d91f234497dfbceba5e251dee"><div class="ttname"><a href="namespacearmnn.xhtml#affec174d91f234497dfbceba5e251dee">armnn::TransposeConvolution2dImpl</a></div><div class="ttdeci">void TransposeConvolution2dImpl(const TransposeConvolution2dDescriptor &descriptor, const TensorShape &inputShape, Decoder< float > &inputDecoder, const TensorShape &outputShape, Encoder< float > &outputEncoder, const TensorShape &weightsShape, Decoder< float > &weightsDecoder, Decoder< float > *biasesDecoder)</div><div class="ttdef"><b>Definition:</b> <a href="_transpose_convolution2d_8cpp_source.xhtml#l00015">TransposeConvolution2d.cpp:15</a></div></div> +<div class="ttc" id="aclassarmnn_1_1_decoder_xhtml_aafe0168dd5ece89e7c62e8d83a4e57cd"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml#aafe0168dd5ece89e7c62e8d83a4e57cd">armnn::Decoder::DecodeTensor</a></div><div class="ttdeci">virtual std::vector< float > DecodeTensor(const TensorShape &tensorShape, bool isDepthwise=false)=0</div></div> +<div class="ttc" id="aclassarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div> <!-- start footer part --> <div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> <ul> <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_efae4012d0e357ebeaba7d02491d70e5.xhtml">reference</a></li><li class="navelem"><a class="el" href="dir_d2f3b8e2e64df3181ebe92efcc0a3012.xhtml">workloads</a></li><li class="navelem"><a class="el" href="_transpose_convolution2d_8cpp.xhtml">TransposeConvolution2d.cpp</a></li> - <li class="footer">Generated on Fri Feb 24 2023 10:24:27 for ArmNN by + <li class="footer">Generated on Wed Mar 22 2023 15:53:02 for ArmNN by <a href="http://www.doxygen.org/index.html"> - <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 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