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<div class="title">FoldPadIntoLayer2d.hpp</div> </div>
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<a href="_fold_pad_into_layer2d_8hpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#pragma once</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="_optimization_8hpp.xhtml">Optimization.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="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">armnnUtils/QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_polymorphic_downcast_8hpp.xhtml">armnn/utility/PolymorphicDowncast.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> {</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">namespace </span>optimizations</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> {</div><div class="line"><a name="l00019"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml"> 19</a></span> <span class="keyword">namespace </span>pad_fold</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> {</div><div class="line"><a name="l00021"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a41605a45fe3f148071b04c7d861f391f"> 21</a></span> <span class="keyword">inline</span> <span class="keywordtype">float</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> {</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>() ? tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() : 0);</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> </div><div class="line"><a name="l00026"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a1112c7c010be092e8d2478e5268666de"> 26</a></span> <span class="keyword">inline</span> <span class="keywordtype">float</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a1112c7c010be092e8d2478e5268666de">GetLowestElement</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</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>  constexpr <span class="keywordtype">float</span> negativeInfinity = -std::numeric_limits<float>::infinity();</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> scale = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>();</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> int32_t offset = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>();</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordflow">switch</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</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="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>:</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize<armnn::Half>(negativeInfinity, scale, offset);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>:</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize<float>(negativeInfinity, scale, offset);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>:</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize<uint8_t>(negativeInfinity, scale, offset);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>:</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize<int16_t>(negativeInfinity, scale, offset);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>:</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">// Fall-through</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>:</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize<int8_t>(negativeInfinity, scale, offset);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>:</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize<armnn::BFloat16>(negativeInfinity, scale, offset);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">default</span>:</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>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Unsupported DataType"</span>);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">return</span> NAN;</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>  }</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> </div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#aef8fbdfbe08862db57b8ea6e09d84bce"> 56</a></span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#aef8fbdfbe08862db57b8ea6e09d84bce">IsNeutralElement</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a>&, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo, <span class="keyword">const</span> <span class="keywordtype">float</span> tensorValue)</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">return</span> tensorValue == <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a>(tensorInfo);</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> </div><div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a9c5795e478ba9afc068c645f3ac72ca5"> 61</a></span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#aef8fbdfbe08862db57b8ea6e09d84bce">IsNeutralElement</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a>&,</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> tensorValue)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">return</span> tensorValue == <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a>(tensorInfo);</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> </div><div class="line"><a name="l00068"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a860dc7bc83a72db266ef5d6759686d24"> 68</a></span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#aef8fbdfbe08862db57b8ea6e09d84bce">IsNeutralElement</a>(</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a>& descriptor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo, <span class="keyword">const</span> <span class="keywordtype">float</span> tensorValue)</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">return</span> (descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> == <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">PoolingAlgorithm::Max</a>)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  ? tensorValue <= <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a1112c7c010be092e8d2478e5268666de">GetLowestElement</a>(tensorInfo)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  : tensorValue == <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a>(tensorInfo);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a9f3ad988ab5cf0c11de5380e77bbb50e"> 76</a></span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a9f3ad988ab5cf0c11de5380e77bbb50e">IsPooling2dPadded</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a>& poolDescriptor)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">const</span> <span class="keyword">auto</span> poolingPadValues = std::make_tuple(poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">if</span> (poolingPadValues != std::make_tuple(0U, 0U, 0U, 0U))</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</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">return</span> <span class="keyword">false</span>;</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> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Descriptor></div><div class="line"><a name="l00088"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a33ffc65d1f6581b0789d3d3a033f698e"> 88</a></span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a33ffc65d1f6581b0789d3d3a033f698e">TryFoldPadIntoLayer2d</a>(</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a>& padDescriptor, Descriptor& layerDescriptor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</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>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> layout = <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a>(layerDescriptor.m_DataLayout);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchIndex = 0;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  constexpr <span class="keyword">auto</span> noPad = std::make_pair(0U, 0U);</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>  <span class="keywordflow">if</span> ((!<a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#aef8fbdfbe08862db57b8ea6e09d84bce">IsNeutralElement</a>(layerDescriptor, tensorInfo, padDescriptor.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">m_PadValue</a>)) ||</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  (padDescriptor.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a>[batchIndex] != noPad) || (padDescriptor.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a>[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()] != noPad))</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>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</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> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& padList = padDescriptor.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a>;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// In Convolution2dDescriptor/Pooling2dDescriptor, padLeft and padRight are defined as paddings</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// on width dimension whereas padTop and padBottom - paddings on height dimension, so updating</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// these according to data layout</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  layerDescriptor.m_PadLeft += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()].first;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  layerDescriptor.m_PadRight += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()].second;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  layerDescriptor.m_PadTop += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()].first;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  layerDescriptor.m_PadBottom += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()].second;</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>  <span class="keywordflow">return</span> <span class="keyword">true</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"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#adeaaacf15ed6830d77298930545187e6"> 115</a></span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a33ffc65d1f6581b0789d3d3a033f698e">TryFoldPadIntoLayer2d</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a>& padDescriptor,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a>& poolDescriptor,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordtype">bool</span> isBackendOptimization = <span class="keyword">false</span>)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// Cannot fold Average or L2 pooling if padding exists and the padding method is Exclude.</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">if</span> (poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> != <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">PoolingAlgorithm::Max</a> &&</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a9f3ad988ab5cf0c11de5380e77bbb50e">IsPooling2dPadded</a>(poolDescriptor) &&</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> == <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>)</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>  <span class="keywordflow">return</span> <span class="keyword">false</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>  <span class="comment">// Cannot fold Average pooling if data type is quantized and layout is NHWC in Neon backend.</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Therefore, this specific case will become a backend specific optimization.</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">if</span> (!isBackendOptimization &&</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>() &&</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> == <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">PoolingAlgorithm::Average</a> &&</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  }</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>  poolDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">PaddingMethod::IgnoreValue</a>;</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>  <span class="keywordflow">return</span> TryFoldPadIntoLayer2d<Pooling2dDescriptor>(padDescriptor, poolDescriptor, tensorInfo);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> }</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="keyword">template</span> <<span class="keyword">typename</span> Layer2dT></div><div class="line"><a name="l00144"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a0dfb192db7209941d02bba0bd904822e"> 144</a></span> Layer2dT* <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a0dfb192db7209941d02bba0bd904822e">FoldPadIntoLayer2dImpl</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph, <a class="code" href="classarmnn_1_1_input_slot.xhtml">InputSlot</a>& connection)</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> {</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>& padLayer = *PolymorphicDowncast<PadLayer*>(&connection.<a class="code" href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>());</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  Layer2dT& layer2d = *PolymorphicDowncast<Layer2dT*>(&connection.<a class="code" href="classarmnn_1_1_input_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</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>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a>& padDescriptor = padLayer.<a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">GetParameters</a>();</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keyword">auto</span> newLayer2dDescriptor = layer2d.GetParameters();</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">if</span> (!<a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a33ffc65d1f6581b0789d3d3a033f698e">TryFoldPadIntoLayer2d</a>(padDescriptor, newLayer2dDescriptor, padLayer.<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>()))</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  {</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</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> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="comment">// Save original parent output slot of the pad layer</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="classarmnn_1_1_output_slot.xhtml">OutputSlot</a>& parentSlot = *padLayer.<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>();</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>  <span class="comment">// Insert new layer2d layer between the pad layer an its parent layer.</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">const</span> std::string name = std::string(<span class="stringliteral">"folded-"</span>) + padLayer.<a class="code" href="classarmnn_1_1_layer.xhtml#a7ddf0cf6f620d59c10e63495ace795d0">GetName</a>() + <span class="stringliteral">"-into-"</span> + layer2d.GetName();</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">auto</span>& newLayer2d = *graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a3ff30c6669fdc69de1f5be1f89bacc3f">InsertNewLayer</a><Layer2dT>(padLayer.<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0), newLayer2dDescriptor, name.c_str());</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  newLayer2d.GetOutputSlot().MoveAllConnections(parentSlot);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="comment">// Start at 1 to connect only weights and bias</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1; i < layer2d.GetNumInputSlots(); ++i)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">if</span> (layer2d.GetInputSlot(i).GetConnectedOutputSlot() != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>& tgtLayer = layer2d.<a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(i).<a class="code" href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>();</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">// Ensure we are definitely connecting the necessary constant layers</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">if</span> (tgtLayer.<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a>)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="comment">// Remove old connection and connect to new layer2d</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  tgtLayer.<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#ac72a192dfcfa19e6ce826f99b415a11d">Disconnect</a>(layer2d.GetInputSlot(i));</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  tgtLayer.<a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(newLayer2d.GetInputSlot(i));</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// Moves connections in old layer2d layer output to new layer.</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// Old layer2d layer will be removed as it's left unconnected.</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// Pad layer will be removed if left unconnected.</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  layer2d.GetOutputSlot().MoveAllConnections(newLayer2d.GetOutputSlot());</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">return</span> &newLayer2d;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> }</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml"> 189</a></span> <span class="keyword">class </span><a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml">FoldPadIntoConvolution2dImpl</a></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00192"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de"> 192</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de">Run</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph, <a class="code" href="classarmnn_1_1_input_slot.xhtml">InputSlot</a>& connection)<span class="keyword"> const</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">const</span> <span class="keyword">auto</span> newConv2dLayer = FoldPadIntoLayer2dImpl<Convolution2dLayer>(graph, connection);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">if</span> (newConv2dLayer != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer = PolymorphicDowncast<Convolution2dLayer*>(&connection.<a class="code" href="classarmnn_1_1_input_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>());</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="comment">// Copy weights and bias to the new convolution layer</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(newConv2dLayer->GetInputSlot(1).GetConnection() != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="stringliteral">"FoldPadIntoConvolution2d: New convolution layer is missing connection to weights layer"</span>);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="comment">// Deprecated 22.11</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  newConv2dLayer->m_Weight = std::move(conv2dLayer->m_Weight);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">if</span> (conv2dLayer->GetParameters().m_BiasEnabled)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(newConv2dLayer->GetInputSlot(2).GetConnection() != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="stringliteral">"FoldPadIntoConvolution2d: New convolution layer is missing "</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="stringliteral">"connection to bias layer."</span>);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">// Deprecated 22.11</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  newConv2dLayer->m_Bias = std::move(conv2dLayer->m_Bias);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  }</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml#a008dac8de51c0f701621e64c91c6b9f8">FoldPadIntoConvolution2dImpl</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml#ab52c3ac73b3b657ccc44ac92d2ce88f1">~FoldPadIntoConvolution2dImpl</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> };</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml"> 223</a></span> <span class="keyword">class </span><a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml">FoldPadIntoDepthwiseConvolution2dImpl</a></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00226"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de"> 226</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de">Run</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph, <a class="code" href="classarmnn_1_1_input_slot.xhtml">InputSlot</a>& connection)<span class="keyword"> const</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> <span class="keyword">auto</span> newConv2dLayer = FoldPadIntoLayer2dImpl<DepthwiseConvolution2dLayer>(graph, connection);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">if</span> (newConv2dLayer != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer = PolymorphicDowncast<DepthwiseConvolution2dLayer*>(&connection.<a class="code" href="classarmnn_1_1_input_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>());</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// Copy weights and bias to the new convolution layer</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(newConv2dLayer->GetInputSlot(1).GetConnection() != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="stringliteral">"FoldPadIntoDepthwiseConvolution2d: New convolution layer is missing connection to weights layer"</span>);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// Deprecated 22.11</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  newConv2dLayer->m_Weight = std::move(conv2dLayer->m_Weight);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">if</span> (conv2dLayer->GetParameters().m_BiasEnabled)</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  {</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(newConv2dLayer->GetInputSlot(2).GetConnection() != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="stringliteral">"FoldPadIntoConvolution2d: New convolution layer is missing "</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="stringliteral">"connection to bias layer."</span>);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="comment">// Deprecated 22.11</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  newConv2dLayer->m_Bias = std::move(conv2dLayer->m_Bias);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  }</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml">FoldPadIntoDepthwiseConvolution2dImpl</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  ~<a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml">FoldPadIntoDepthwiseConvolution2dImpl</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> };</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml"> 256</a></span> <span class="keyword">class </span><a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml">FoldPadIntoPooling2dImpl</a></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> {</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00259"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de"> 259</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de">Run</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph, <a class="code" href="classarmnn_1_1_input_slot.xhtml">InputSlot</a>& connection)<span class="keyword"> const</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  FoldPadIntoLayer2dImpl<Pooling2dLayer>(graph, connection);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  }</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml">FoldPadIntoPooling2dImpl</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  ~<a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml">FoldPadIntoPooling2dImpl</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> };</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> } <span class="comment">// namespace pad_fold</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="keyword">using</span> <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.xhtml">FoldPadIntoConvolution2d</a> =</div><div class="line"><a name="l00271"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations.xhtml#a8b394ff60ed829a91f07deac476f3db2"> 271</a></span>  <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.xhtml">OptimizeForExclusiveConnection<PadLayer, Convolution2dLayer, pad_fold::FoldPadIntoConvolution2dImpl></a>;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="keyword">using</span> <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.xhtml">FoldPadIntoDepthwiseConvolution2d</a> =</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.xhtml">OptimizeForExclusiveConnection</a> <<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>,</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>,</div><div class="line"><a name="l00275"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations.xhtml#a227e9ab5e488aa90ba462790ba0e5aec"> 275</a></span>  <a class="code" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml">pad_fold::FoldPadIntoDepthwiseConvolution2dImpl</a>>;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="keyword">using</span> <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.xhtml">FoldPadIntoPooling2d</a> =</div><div class="line"><a name="l00277"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations.xhtml#a279d0a7c56966cea334303d48a874964"> 277</a></span>  <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.xhtml">OptimizeForExclusiveConnection<PadLayer, Pooling2dLayer, pad_fold::FoldPadIntoPooling2dImpl></a>;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> } <span class="comment">// namespace optimizations</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> } <span class="comment">// namespace armnn</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </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="structarmnn_1_1_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00374">Descriptors.hpp:374</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="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl_xhtml_a008dac8de51c0f701621e64c91c6b9f8"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml#a008dac8de51c0f701621e64c91c6b9f8">armnn::optimizations::pad_fold::FoldPadIntoConvolution2dImpl::FoldPadIntoConvolution2dImpl</a></div><div class="ttdeci">FoldPadIntoConvolution2dImpl()=default</div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::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#l00368">Descriptors.hpp:368</a></div></div>
<div class="ttc" id="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl_xhtml_a5a8476ffc04ce7460bb09ad50d1d23de"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de">armnn::optimizations::pad_fold::FoldPadIntoConvolution2dImpl::Run</a></div><div class="ttdeci">void Run(Graph &graph, InputSlot &connection) const</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00192">FoldPadIntoLayer2d.hpp:192</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml_a410fa919f78af0f0f100bd1594eca4ab"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">armnn::PadDescriptor::m_PadValue</a></div><div class="ttdeci">float m_PadValue</div><div class="ttdoc">Optional value to use for padding, defaults to 0. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01198">Descriptors.hpp:1198</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_1_1pad__fold_xhtml_a0dfb192db7209941d02bba0bd904822e"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a0dfb192db7209941d02bba0bd904822e">armnn::optimizations::pad_fold::FoldPadIntoLayer2dImpl</a></div><div class="ttdeci">Layer2dT * FoldPadIntoLayer2dImpl(Graph &graph, InputSlot &connection)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00144">FoldPadIntoLayer2d.hpp:144</a></div></div>
<div class="ttc" id="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl_xhtml_a5a8476ffc04ce7460bb09ad50d1d23de"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de">armnn::optimizations::pad_fold::FoldPadIntoPooling2dImpl::Run</a></div><div class="ttdeci">void Run(Graph &graph, InputSlot &connection) const</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00259">FoldPadIntoLayer2d.hpp:259</a></div></div>
<div class="ttc" id="classarmnn_1_1_depthwise_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">armnn::DepthwiseConvolution2dLayer</a></div><div class="ttdoc">This layer represents a depthwise convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00015">DepthwiseConvolution2dLayer.hpp:15</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00495">Descriptors.hpp:495</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_a7ddaf04177053a536f0e7be83a642bc6"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">armnn::OutputSlot::GetOwningLayer</a></div><div class="ttdeci">Layer & GetOwningLayer() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00119">Layer.hpp:119</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_adcfb97035799ea4c043f9ef370714815"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">armnn::OutputSlot::Connect</a></div><div class="ttdeci">int Connect(InputSlot &destination)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00112">Layer.cpp:112</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&#39;t count and are ignored. </div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling2dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00386">Descriptors.hpp:386</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::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#l00372">Descriptors.hpp:372</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_input_slot.xhtml">armnn::InputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00042">Layer.hpp:42</a></div></div>
<div class="ttc" id="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl_xhtml"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml">armnn::optimizations::pad_fold::FoldPadIntoConvolution2dImpl</a></div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00189">FoldPadIntoLayer2d.hpp:189</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml_a85f98c94e11f65a6b73f831735c040f3"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">armnn::PadDescriptor::m_PadList</a></div><div class="ttdeci">std::vector< std::pair< unsigned int, unsigned int > > m_PadList</div><div class="ttdoc">Specifies the padding for input dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01195">Descriptors.hpp:1195</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_layer_with_parameters_xhtml_afa3e8a8f23589b1eaddbe203825bbdcf"><div class="ttname"><a href="classarmnn_1_1_layer_with_parameters.xhtml#afa3e8a8f23589b1eaddbe203825bbdcf">armnn::LayerWithParameters::GetParameters</a></div><div class="ttdeci">const Parameters & GetParameters() const override</div><div class="ttdoc">If the layer has a descriptor return it. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_with_parameters_8hpp_source.xhtml#l00019">LayerWithParameters.hpp:19</a></div></div>
<div class="ttc" id="classarmnn_1_1_pad_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_pad_layer.xhtml">armnn::PadLayer</a></div><div class="ttdoc">This layer represents a pad operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pad_layer_8hpp_source.xhtml#l00014">PadLayer.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl_xhtml"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.xhtml">armnn::optimizations::pad_fold::FoldPadIntoPooling2dImpl</a></div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00256">FoldPadIntoLayer2d.hpp:256</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_ac72a192dfcfa19e6ce826f99b415a11d"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#ac72a192dfcfa19e6ce826f99b415a11d">armnn::OutputSlot::Disconnect</a></div><div class="ttdeci">void Disconnect(InputSlot &slot)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00120">Layer.cpp:120</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="_polymorphic_downcast_8hpp_xhtml"><div class="ttname"><a href="_polymorphic_downcast_8hpp.xhtml">PolymorphicDowncast.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01173">Descriptors.hpp:1173</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">armnn::Layer::GetInputSlot</a></div><div class="ttdeci">const InputSlot & GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00324">Layer.hpp:324</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_1_1pad__fold_xhtml_a33ffc65d1f6581b0789d3d3a033f698e"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a33ffc65d1f6581b0789d3d3a033f698e">armnn::optimizations::pad_fold::TryFoldPadIntoLayer2d</a></div><div class="ttdeci">bool TryFoldPadIntoLayer2d(const PadDescriptor &padDescriptor, Descriptor &layerDescriptor, const TensorInfo &tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00088">FoldPadIntoLayer2d.hpp:88</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00370">Descriptors.hpp:370</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00478">Tensor.cpp:478</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00461">Tensor.cpp:461</a></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_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00198">Tensor.hpp:198</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_1_1pad__fold_xhtml_a9f3ad988ab5cf0c11de5380e77bbb50e"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a9f3ad988ab5cf0c11de5380e77bbb50e">armnn::optimizations::pad_fold::IsPooling2dPadded</a></div><div class="ttdeci">bool IsPooling2dPadded(const Pooling2dDescriptor &poolDescriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00076">FoldPadIntoLayer2d.hpp:76</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_ad8e15c530c929ab823d89ae9fd2d3f11"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">armnn::Layer::GetType</a></div><div class="ttdeci">LayerType GetType() const override</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00273">Layer.hpp:273</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00087">Layer.hpp:87</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_slot_xhtml_a9effd325a6d512a3f8ff4bd207d53255"><div class="ttname"><a href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">armnn::InputSlot::GetConnectedOutputSlot</a></div><div class="ttdeci">const OutputSlot * GetConnectedOutputSlot() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00056">Layer.hpp:56</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_1_1pad__fold_xhtml_a41605a45fe3f148071b04c7d861f391f"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a41605a45fe3f148071b04c7d861f391f">armnn::optimizations::pad_fold::GetZeroElement</a></div><div class="ttdeci">float GetZeroElement(const TensorInfo &tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00021">FoldPadIntoLayer2d.hpp:21</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_slot_xhtml_a7ddaf04177053a536f0e7be83a642bc6"><div class="ttname"><a href="classarmnn_1_1_input_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">armnn::InputSlot::GetOwningLayer</a></div><div class="ttdeci">Layer & GetOwningLayer() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00053">Layer.hpp:53</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00030">Graph.hpp:30</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::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#l00388">Descriptors.hpp:388</a></div></div>
<div class="ttc" id="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl_xhtml_a5a8476ffc04ce7460bb09ad50d1d23de"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml#a5a8476ffc04ce7460bb09ad50d1d23de">armnn::optimizations::pad_fold::FoldPadIntoDepthwiseConvolution2dImpl::Run</a></div><div class="ttdeci">void Run(Graph &graph, InputSlot &connection) const</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00226">FoldPadIntoLayer2d.hpp:226</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00366">Descriptors.hpp:366</a></div></div>
<div class="ttc" id="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl_xhtml_ab52c3ac73b3b657ccc44ac92d2ce88f1"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.xhtml#ab52c3ac73b3b657ccc44ac92d2ce88f1">armnn::optimizations::pad_fold::FoldPadIntoConvolution2dImpl::~FoldPadIntoConvolution2dImpl</a></div><div class="ttdeci">~FoldPadIntoConvolution2dImpl()=default</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a></div><div class="ttdoc">The padding fields count, but are ignored. </div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_1_1pad__fold_xhtml_a1112c7c010be092e8d2478e5268666de"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#a1112c7c010be092e8d2478e5268666de">armnn::optimizations::pad_fold::GetLowestElement</a></div><div class="ttdeci">float GetLowestElement(const TensorInfo &tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00026">FoldPadIntoLayer2d.hpp:26</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot & GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00326">Layer.hpp:326</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimize_for_exclusive_connection_xhtml"><div class="ttname"><a href="classarmnn_1_1_optimize_for_exclusive_connection.xhtml">armnn::OptimizeForExclusiveConnection</a></div><div class="ttdef"><b>Definition:</b> <a href="_optimization_8hpp_source.xhtml#l00173">Optimization.hpp:173</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a7ddf0cf6f620d59c10e63495ace795d0"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a7ddf0cf6f620d59c10e63495ace795d0">armnn::Layer::GetName</a></div><div class="ttdeci">const char * GetName() const override</div><div class="ttdoc">Returns the name of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00319">Layer.hpp:319</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
<div class="ttc" id="_optimization_8hpp_xhtml"><div class="ttname"><a href="_optimization_8hpp.xhtml">Optimization.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00332">Descriptors.hpp:332</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a3ff30c6669fdc69de1f5be1f89bacc3f"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a3ff30c6669fdc69de1f5be1f89bacc3f">armnn::Graph::InsertNewLayer</a></div><div class="ttdeci">LayerT * InsertNewLayer(InputSlot &insertBefore, Args &&... args)</div><div class="ttdoc">Inserts a new layer between the output slot currently connected to insertBefore and insertBefore itse...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00471">Graph.hpp:471</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::PaddingMode::Constant</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_ada2ad7d1caeeb4ef6195c8925fad6a65"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">armnn::OutputSlot::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00092">Layer.cpp:92</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="classarmnn_1_1_tensor_info_xhtml_a7c00efeb540198b33b8558c76e5cc2dd"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">armnn::TensorInfo::IsQuantized</a></div><div class="ttdeci">bool IsQuantized() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00504">Tensor.cpp:504</a></div></div>
<div class="ttc" id="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl_xhtml"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.xhtml">armnn::optimizations::pad_fold::FoldPadIntoDepthwiseConvolution2dImpl</a></div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00223">FoldPadIntoLayer2d.hpp:223</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00620">Descriptors.hpp:620</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00217">Layer.hpp:217</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="namespacearmnn_1_1optimizations_1_1pad__fold_xhtml_aef8fbdfbe08862db57b8ea6e09d84bce"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.xhtml#aef8fbdfbe08862db57b8ea6e09d84bce">armnn::optimizations::pad_fold::IsNeutralElement</a></div><div class="ttdeci">bool IsNeutralElement(const Convolution2dDescriptor &, const TensorInfo &tensorInfo, const float tensorValue)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.xhtml#l00056">FoldPadIntoLayer2d.hpp:56</a></div></div>
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