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<div class="title">Conv2dOperator.cpp</div> </div>
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<a href="_conv2d_operator_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div>
<div class="line"><a name="l00005"></a><span class="lineno"> 5</span>  </div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_conv2d_operator_8hpp.xhtml">Conv2dOperator.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"><a class="line" href="_conv2d_operator_8hpp.xhtml#a68084d1082900574f7bbc3c1e6a10ba0"> 8</a></span> TosaSerializationBasicBlock* <a class="code" href="_conv2d_operator_8cpp.xhtml#a68084d1082900574f7bbc3c1e6a10ba0">ConvertConv2dToTosaOperator</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* layer,</div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  <span class="keyword">const</span> std::vector<const TensorInfo*>& inputs,</div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span>  <span class="keyword">const</span> std::vector<const TensorInfo*>& outputs,</div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a>* conv2dDescriptor)</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>  std::vector<std::string> inputNames;</div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  std::string outputName = std::string(<span class="stringliteral">"output0_"</span>);</div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  std::string blockName = std::string(<span class="stringliteral">"Op_CONV2D_block_"</span>) + <a class="code" href="_tosa_operator_utils_8hpp.xhtml#aadc5d73bd0cb81999bcfdc62bce020e8">GetUniqueTosaMappingID</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="comment">// Set input names for validation purposes only.</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keywordflow">if</span>(layer == <span class="keyword">nullptr</span>)</div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  {</div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  inputNames.emplace_back(<span class="stringliteral">"input0_"</span>);</div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  inputNames.emplace_back(<span class="stringliteral">"input1_"</span>);</div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keywordflow">if</span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</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>  inputNames.emplace_back(<span class="stringliteral">"input2_"</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"> 26</span>  }</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="comment">// If a layer is present then the block will be used for execution, so input and output names need to be</span></div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="comment">// determined using the previous and following layers so the graph is connected correctly.</span></div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="comment">// For validation this doesn't matter.</span></div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  {</div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// Get the layer connected to the input slot and determine unique tensor names.</span></div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < inputs.size(); ++i)</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>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>& connectedLayer = layer-><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="l00036"></a><span class="lineno"> 36</span>  </div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  std::string inputName = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a1640c964a461e8580837a79829a5c197">GenerateUniqueName</a>(connectedLayer, i);</div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  inputNames.push_back(inputName);</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  }</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  </div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="comment">// Determine unique output tensor name.</span></div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  outputName = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a246662c69dac647833be50ba6dcee024">GenerateUniqueOutputName</a>(*layer, 0);</div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  }</div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  </div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  std::vector<TosaSerializationTensor*> tensors;</div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::vector<TosaSerializationOperator*> operators;</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  </div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Setup input Tensor</span></div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// Only add tensor if connected layer is an input layer.</span></div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// As intermediate or constant tensors will be created separately.</span></div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// There also can't be duplicate tensors.</span></div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">if</span>(inputNames[0].find(<span class="stringliteral">"input0_"</span>) != std::string::npos)</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>  std::vector<int32_t> inputShape0 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[0]->GetShape());</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  DType inputDType0 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[0]->GetDataType());</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  </div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(inputNames[0], inputShape0, inputDType0, {}));</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  </div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="comment">// Only add input tensors if weights and bias are not constant or if running validation.</span></div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Constant tensors will be created in the ConvertConstantToTosaOperator function.</span></div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">if</span>(!inputs[1]->IsConstant() || layer == <span class="keyword">nullptr</span>)</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>  std::vector<int32_t> inputShape1 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[1]->GetShape());</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  DType inputDType1 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[1]->GetDataType());</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(inputNames[1], inputShape1, inputDType1, {}));</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>  </div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">if</span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  {</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">if</span>(!inputs[2]->IsConstant() || layer == <span class="keyword">nullptr</span>)</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  {</div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  std::vector<int32_t> inputShape2 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[2]->GetShape());</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  DType inputDType2 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[2]->GetDataType());</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  </div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(inputNames[2], inputShape2, inputDType2, {}));</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</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="keywordflow">else</span></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="comment">// If bias is disabled, create a constant bias of 0 as three inputs are required.</span></div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::string constantName = std::string(<span class="stringliteral">"constant_"</span>) + <a class="code" href="_tosa_operator_utils_8hpp.xhtml#aadc5d73bd0cb81999bcfdc62bce020e8">GetUniqueTosaMappingID</a>();</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  </div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  operators.push_back(<span class="keyword">new</span> TosaSerializationOperator(Op_CONST, Attribute_NONE, <span class="keyword">nullptr</span>, {}, {constantName}));</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="comment">// The size of the bias must match the channels dimension, so get the correct index.</span></div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = (conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == DataLayout::NHWC) ? 3 : 1;</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  </div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  std::vector<uint8_t> uint8Data;</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  std::vector<float> data(outputs[0]->GetShape()[index], 0.0f);</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  </div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  TosaSerializationHandler::ConvertF32toU8(data, uint8Data);</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  </div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(constantName,</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  {<span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(outputs[0]->GetShape()[index])},</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  DType_FP32,</div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  uint8Data));</div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  inputNames.emplace_back(constantName);</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="comment">// Setup Output Tensor</span></div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  std::vector<int32_t> outputShape0 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(outputs[0]->GetShape());</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  DType outputDType0 = <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(outputs[0]->GetDataType());</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  </div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));</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>  <span class="comment">// Set up CONV2D operator</span></div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::vector<int> pad = {<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>),</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>),</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>),</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>)};</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::vector<int> stride = {<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>),</div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)};</div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  std::vector<int> dilation = {<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>),</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(conv2dDescriptor-><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>)};</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  TosaConvAttribute attribute(pad, stride, dilation, 0, 0, <a class="code" href="_tosa_operator_utils_8hpp.xhtml#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[0]->GetDataType()));</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>  <span class="keyword">auto</span>* op = <span class="keyword">new</span> TosaSerializationOperator(Op_CONV2D,</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  Attribute_ConvAttribute,</div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  &attribute,</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  inputNames,</div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  {outputName});</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  operators.push_back(op);</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>  <span class="comment">// operatorInputNames/operatorOutputNames ends up being the same as</span></div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings</span></div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> TosaSerializationBasicBlock(blockName, <span class="comment">// name</span></div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  operators, <span class="comment">// operators</span></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  tensors, <span class="comment">// tensors</span></div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  inputNames, <span class="comment">// inputs</span></div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {outputName}); <span class="comment">// outputs</span></div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span> }</div>
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<div class="ttc" id="a_tosa_operator_utils_8hpp_xhtml_a246662c69dac647833be50ba6dcee024"><div class="ttname"><a href="_tosa_operator_utils_8hpp.xhtml#a246662c69dac647833be50ba6dcee024">GenerateUniqueOutputName</a></div><div class="ttdeci">std::string GenerateUniqueOutputName(const Layer &layer, uint32_t layerSlot)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.xhtml#l00079">TosaOperatorUtils.hpp:79</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::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#l00552">Descriptors.hpp:552</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::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#l00550">Descriptors.hpp:550</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00548">Descriptors.hpp:548</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::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#l00540">Descriptors.hpp:540</a></div></div>
<div class="ttc" id="aclassarmnn_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="aclassarmnn_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="astructarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00546">Descriptors.hpp:546</a></div></div>
<div class="ttc" id="a_tosa_operator_utils_8hpp_xhtml_a7d97964a65be4eb4a5b4109904f2e7f7"><div class="ttname"><a href="_tosa_operator_utils_8hpp.xhtml#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a></div><div class="ttdeci">std::vector< int32_t > GetTosaTensorShape(const TensorShape &shape)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.xhtml#l00049">TosaOperatorUtils.hpp:49</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::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#l00538">Descriptors.hpp:538</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::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#l00536">Descriptors.hpp:536</a></div></div>
<div class="ttc" id="a_conv2d_operator_8hpp_xhtml"><div class="ttname"><a href="_conv2d_operator_8hpp.xhtml">Conv2dOperator.hpp</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::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#l00534">Descriptors.hpp:534</a></div></div>
<div class="ttc" id="astructarmnn_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#l00502">Descriptors.hpp:502</a></div></div>
<div class="ttc" id="a_tosa_operator_utils_8hpp_xhtml_a1640c964a461e8580837a79829a5c197"><div class="ttname"><a href="_tosa_operator_utils_8hpp.xhtml#a1640c964a461e8580837a79829a5c197">GenerateUniqueName</a></div><div class="ttdeci">std::string GenerateUniqueName(const Layer &layer, uint32_t layerSlot)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.xhtml#l00060">TosaOperatorUtils.hpp:60</a></div></div>
<div class="ttc" id="aclassarmnn_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="a_tosa_operator_utils_8hpp_xhtml_a45d66f17ad6b0469e469f443b3e03226"><div class="ttname"><a href="_tosa_operator_utils_8hpp.xhtml#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a></div><div class="ttdeci">DType ArmNNToDType(const DataType &type)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.xhtml#l00020">TosaOperatorUtils.hpp:20</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::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#l00542">Descriptors.hpp:542</a></div></div>
<div class="ttc" id="aclassarmnn_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="a_tosa_operator_utils_8hpp_xhtml_aadc5d73bd0cb81999bcfdc62bce020e8"><div class="ttname"><a href="_tosa_operator_utils_8hpp.xhtml#aadc5d73bd0cb81999bcfdc62bce020e8">GetUniqueTosaMappingID</a></div><div class="ttdeci">std::string GetUniqueTosaMappingID()</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.xhtml#l00097">TosaOperatorUtils.hpp:97</a></div></div>
<div class="ttc" id="a_conv2d_operator_8cpp_xhtml_a68084d1082900574f7bbc3c1e6a10ba0"><div class="ttname"><a href="_conv2d_operator_8cpp.xhtml#a68084d1082900574f7bbc3c1e6a10ba0">ConvertConv2dToTosaOperator</a></div><div class="ttdeci">TosaSerializationBasicBlock * ConvertConv2dToTosaOperator(const Layer *layer, const std::vector< const TensorInfo * > &inputs, const std::vector< const TensorInfo * > &outputs, const Convolution2dDescriptor *conv2dDescriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_operator_8cpp_source.xhtml#l00008">Conv2dOperator.cpp:8</a></div></div>
<div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::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#l00544">Descriptors.hpp:544</a></div></div>
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