IVGCVSW-8260 Update Doxgen Docu for 24.05
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: If4bc983bf2793a27ded8e26ac2b29523fc1e4711
diff --git a/latest/_transpose_conv2d_operator_8cpp_source.html b/latest/_transpose_conv2d_operator_8cpp_source.html
index 7a8e2c9..ff45703 100644
--- a/latest/_transpose_conv2d_operator_8cpp_source.html
+++ b/latest/_transpose_conv2d_operator_8cpp_source.html
@@ -36,7 +36,7 @@
<img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 15rem; margin-top: .5rem; margin-left 13px"/>
<td id="projectalign" style="padding-left: 0.9em;">
<div id="projectname">
-  <span id="projectnumber">24.02</span>
+  <span id="projectnumber">24.05</span>
</div>
</td>
</tr>
@@ -97,7 +97,7 @@
</div><!--header-->
<div class="contents">
<a href="_transpose_conv2d_operator_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div>
-<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.</span></div>
+<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2022-2024 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>
@@ -110,7 +110,7 @@
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span>  <span class="keyword">const</span> std::vector<const TensorInfo*>& outputs,</div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html">TransposeConvolution2dDescriptor</a>* descriptor)</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>  std::string input0Name = std::string(<span class="stringliteral">"input0_"</span>);</div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  std::string input0Name = std::string(<span class="stringliteral">"input_"</span>);</div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  std::string input1Name = std::string(<span class="stringliteral">"constant_"</span>) + <a class="code" href="_tosa_operator_utils_8hpp.html#aadc5d73bd0cb81999bcfdc62bce020e8">GetUniqueTosaMappingID</a>();</div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  std::string input2Name = std::string(<span class="stringliteral">"constant_"</span>) + <a class="code" href="_tosa_operator_utils_8hpp.html#aadc5d73bd0cb81999bcfdc62bce020e8">GetUniqueTosaMappingID</a>();</div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  std::string outputName = std::string(<span class="stringliteral">"output0_"</span>);</div>
@@ -120,176 +120,170 @@
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="comment">// using the previous and following layers so the graph is connected correctly. For validation this doesn't matter.</span></div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keywordflow">if</span>(layer != <span class="keyword">nullptr</span>)</div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  {</div>
-<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="comment">// Get the layers connected to the input slots and determine unique tensor names.</span></div>
-<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <a class="code" href="classarmnn_1_1_layer.html">Layer</a>& connectedInputLayer = layer-><a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>();</div>
-<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  input0Name = <a class="code" href="_tosa_operator_utils_8hpp.html#a1640c964a461e8580837a79829a5c197">GenerateUniqueName</a>(connectedInputLayer, 0);</div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  input0Name = <a class="code" href="_tosa_operator_utils_8hpp.html#a8863b57c08a748fd2dd16880337f4b69">GenerateUniqueInputName</a>(layer-><a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  outputName = <a class="code" href="_tosa_operator_utils_8hpp.html#a1e5b1f8dcc21f10bc7b0d8517e05049d">GenerateUniqueOutputName</a>(*layer);</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>  </div>
-<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="comment">// Determine unique output tensor name.</span></div>
-<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  outputName = <a class="code" href="_tosa_operator_utils_8hpp.html#a246662c69dac647833be50ba6dcee024">GenerateUniqueOutputName</a>(*layer, 0);</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>  </div>
-<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  std::vector<TosaSerializationTensor*> tensors;</div>
-<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  std::vector<TosaSerializationOperator*> operators;</div>
-<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  </div>
-<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// Setup input tensor</span></div>
-<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="comment">// Only add tensor if connected layer is an input layer.</span></div>
-<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="comment">// As intermediate or constant tensors will be created separately.</span></div>
-<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="comment">// There also can't be duplicate tensors.</span></div>
-<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordflow">if</span>(input0Name.find(<span class="stringliteral">"input0_"</span>) != std::string::npos)</div>
-<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  {</div>
-<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  std::vector<int32_t> inputShape0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[0]->GetShape());</div>
-<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  DType inputDType0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[0]->GetDataType());</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}));</div>
-<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  }</div>
-<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  </div>
-<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Setup weights tensor, constant data will get copied during SetConstantTensorData</span></div>
-<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  operators.push_back(<span class="keyword">new</span> TosaSerializationOperator(Op_CONST, Attribute_NONE, <span class="keyword">nullptr</span>, {}, {input1Name}));</div>
-<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  </div>
-<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// During validation the TensorInfo can be retrieved from the inputs.</span></div>
-<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// During execution, it is only available through the layer so use m_Weight.</span></div>
-<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">if</span>(layer == <span class="keyword">nullptr</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>  std::vector<int32_t> inputShape1 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[1]->GetShape());</div>
-<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  DType inputDType1 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[1]->GetDataType());</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input1Name, inputShape1, inputDType1, {}));</div>
-<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</div>
-<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">else</span></div>
-<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div>
-<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">auto</span> transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer);</div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  std::vector<TosaSerializationTensor*> tensors;</div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  std::vector<TosaSerializationOperator*> operators;</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">// Setup input tensor</span></div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="comment">// Only add tensor if connected layer is an input layer.</span></div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="comment">// As intermediate or constant tensors will be created separately.</span></div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// There also can't be duplicate tensors.</span></div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">if</span>(input0Name.find(<span class="stringliteral">"input_"</span>) != std::string::npos)</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  {</div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  std::vector<int32_t> inputShape0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[0]->GetShape());</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  DType inputDType0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[0]->GetDataType());</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}));</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  }</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  </div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Setup weights tensor, constant data will get copied during SetConstantTensorData</span></div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  operators.push_back(<span class="keyword">new</span> TosaSerializationOperator(Op_CONST, Attribute_NONE, <span class="keyword">nullptr</span>, {}, {input1Name}));</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  </div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// During validation the TensorInfo can be retrieved from the inputs.</span></div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// During execution, it is only available through the layer so use m_Weight.</span></div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">if</span>(layer == <span class="keyword">nullptr</span>)</div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  {</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  std::vector<int32_t> inputShape1 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[1]->GetShape());</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  DType inputDType1 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[1]->GetDataType());</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input1Name, inputShape1, inputDType1, {}));</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  }</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">else</span></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="keyword">auto</span> transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer);</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>  std::vector<int32_t> inputShape1 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  transposeConv2dLayer->m_Weight->GetTensorInfo().GetShape());</div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  DType inputDType1 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(transposeConv2dLayer->m_Weight->GetTensorInfo().GetDataType());</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.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(</div>
-<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  transposeConv2dLayer->m_Weight->GetTensorInfo().GetShape());</div>
-<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  DType inputDType1 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(transposeConv2dLayer->m_Weight->GetTensorInfo().GetDataType());</div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  std::vector<uint8_t> uint8Data = <a class="code" href="_tosa_operator_utils_8hpp.html#ae03fdd5482e75faa3b846bd021678f3b">ConvertConstantTensorDataToBuffer</a>(transposeConv2dLayer->m_Weight);</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input1Name, inputShape1, inputDType1, uint8Data));</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"> 68</span>  std::vector<uint8_t> uint8Data = <a class="code" href="_tosa_operator_utils_8hpp.html#ae03fdd5482e75faa3b846bd021678f3b">ConvertConstantTensorDataToBuffer</a>(transposeConv2dLayer->m_Weight);</div>
-<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input1Name, inputShape1, inputDType1, uint8Data));</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>  </div>
-<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="comment">// Setup bias operator and tensor, constant data will get copied during SetConstantTensorData</span></div>
-<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  operators.push_back(<span class="keyword">new</span> TosaSerializationOperator(Op_CONST, Attribute_NONE, <span class="keyword">nullptr</span>, {}, {input2Name}));</div>
-<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  </div>
-<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// During validation the TensorInfo can be retrieved from the inputs.</span></div>
-<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// During execution, it is only available through the layer so use m_Bias.</span></div>
-<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">if</span>(layer == <span class="keyword">nullptr</span> && descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</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>  std::vector<int32_t> inputShape2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[2]->GetShape());</div>
-<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  DType inputDType2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[2]->GetDataType());</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input2Name, inputShape2, inputDType2, {}));</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">else</span> <span class="keywordflow">if</span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div>
-<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  {</div>
-<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">auto</span> transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer);</div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// Setup bias operator and tensor, constant data will get copied during SetConstantTensorData</span></div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  operators.push_back(<span class="keyword">new</span> TosaSerializationOperator(Op_CONST, Attribute_NONE, <span class="keyword">nullptr</span>, {}, {input2Name}));</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="comment">// During validation the TensorInfo can be retrieved from the inputs.</span></div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="comment">// During execution, it is only available through the layer so use m_Bias.</span></div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">if</span>(layer == <span class="keyword">nullptr</span> && descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</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>  std::vector<int32_t> inputShape2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(inputs[2]->GetShape());</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  DType inputDType2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(inputs[2]->GetDataType());</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input2Name, inputShape2, inputDType2, {}));</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> <span class="keywordflow">if</span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</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="keyword">auto</span> transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer);</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>  std::vector<int32_t> inputShape2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  transposeConv2dLayer->m_Bias->GetTensorInfo().GetShape());</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  DType inputDType2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(transposeConv2dLayer->m_Bias->GetTensorInfo().GetDataType());</div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  </div>
-<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  std::vector<int32_t> inputShape2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(</div>
-<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  transposeConv2dLayer->m_Bias->GetTensorInfo().GetShape());</div>
-<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  DType inputDType2 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(transposeConv2dLayer->m_Bias->GetTensorInfo().GetDataType());</div>
-<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  </div>
-<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::vector<uint8_t> uint8Data = <a class="code" href="_tosa_operator_utils_8hpp.html#ae03fdd5482e75faa3b846bd021678f3b">ConvertConstantTensorDataToBuffer</a>(transposeConv2dLayer->m_Bias);</div>
-<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input2Name, inputShape2, inputDType2, 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>  <span class="keywordflow">else</span></div>
-<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  {</div>
-<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="comment">// If bias is disabled, create a constant bias tensor of 0's as three inputs are required.</span></div>
-<div class="line"><a name="l00098"></a><span class="lineno"> 98</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="l00099"></a><span class="lineno"> 99</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = (descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == DataLayout::NHWC) ? 3 : 1;</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>  std::vector<uint8_t> uint8Data;</div>
-<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  std::vector<float> data(outputs[0]->GetShape()[index], 0.0f);</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>  TosaSerializationHandler::ConvertF32toU8(data, uint8Data);</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(input2Name,</div>
-<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  {<span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(outputs[0]->GetShape()[index])},</div>
-<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  DType_FP32,</div>
-<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  uint8Data));</div>
-<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  }</div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  std::vector<uint8_t> uint8Data = <a class="code" href="_tosa_operator_utils_8hpp.html#ae03fdd5482e75faa3b846bd021678f3b">ConvertConstantTensorDataToBuffer</a>(transposeConv2dLayer->m_Bias);</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input2Name, inputShape2, inputDType2, uint8Data));</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  }</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  {</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// If bias is disabled, create a constant bias tensor of 0's as three inputs are required.</span></div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</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="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = (descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == DataLayout::NHWC) ? 3 : 1;</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  </div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  std::vector<uint8_t> uint8Data;</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::vector<float> data(outputs[0]->GetShape()[index], 0.0f);</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  </div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  TosaSerializationHandler::ConvertF32toU8(data, uint8Data);</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>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(input2Name,</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  {<span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(outputs[0]->GetShape()[index])},</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  DType_FP32,</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  uint8Data));</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  }</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  </div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// Setup Output Tensor</span></div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::vector<int32_t> outputShape0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(outputs[0]->GetShape());</div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  DType outputDType0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(outputs[0]->GetDataType());</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="comment">// Setup Output Tensor</span></div>
-<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::vector<int32_t> outputShape0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a7d97964a65be4eb4a5b4109904f2e7f7">GetTosaTensorShape</a>(outputs[0]->GetShape());</div>
-<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  DType outputDType0 = <a class="code" href="_tosa_operator_utils_8hpp.html#a45d66f17ad6b0469e469f443b3e03226">ArmNNToDType</a>(outputs[0]->GetDataType());</div>
-<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  </div>
-<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));</div>
-<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  </div>
-<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// Set up TRANSPOSE_CONV2D operator</span></div>
-<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// The TOSA Reference Model pads the output shape, so it is added to output shape.</span></div>
-<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// In Arm NN we pad the input shape, so it is taken away.</span></div>
-<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// To offset this the negative padding value can be used.</span></div>
-<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  std::vector<int> pad = {-<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>),</div>
-<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  -<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>),</div>
-<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  -<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>),</div>
-<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  -<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>)};</div>
-<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  std::vector<int> stride = {<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>),</div>
-<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)};</div>
-<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  </div>
-<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::vector<int> outputShape;</div>
-<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// If available use shape in descriptor otherwise use output shape.</span></div>
-<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">if</span> (descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a78f5b213a603b55d0fabea389e89f203">m_OutputShape</a>.size() == 4)</div>
-<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div>
-<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a78f5b213a603b55d0fabea389e89f203">m_OutputShape</a>.size(); ++i)</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>  outputShape.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a78f5b213a603b55d0fabea389e89f203">m_OutputShape</a>[i]));</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>  <span class="keywordflow">else</span></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">for</span> (uint32_t i = 0; i < outputs[0]->GetNumDimensions(); ++i)</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>  outputShape.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(outputs[0]->GetShape()[i]));</div>
-<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</div>
-<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div>
-<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  </div>
-<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  TosaTransposeConvAttribute attribute(pad, stride, outputShape, 0, 0);</div>
-<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  </div>
-<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">auto</span>* op = <span class="keyword">new</span> TosaSerializationOperator(Op_TRANSPOSE_CONV2D,</div>
-<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  Attribute_TransposeConvAttribute,</div>
-<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  &attribute,</div>
-<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  {input0Name, input1Name, input2Name},</div>
-<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {outputName});</div>
-<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  operators.push_back(op);</div>
-<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  </div>
-<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="comment">// operatorInputNames/operatorOutputNames ends up being the same as</span></div>
-<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="comment">// blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings</span></div>
-<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> TosaSerializationBasicBlock(blockName, <span class="comment">// name</span></div>
-<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="_tosa_operator_utils_8hpp.html#a28514b014a4fe0841044f1868064bc65">mainName</a>, <span class="comment">// region name</span></div>
-<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  operators, <span class="comment">// operators</span></div>
-<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  tensors, <span class="comment">// tensors</span></div>
-<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  {input0Name, input1Name, input2Name}, <span class="comment">// inputs</span></div>
-<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {outputName}); <span class="comment">// outputs</span></div>
-<div class="line"><a name="l00163"></a><span class="lineno"> 163</span> }</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  tensors.push_back(<span class="keyword">new</span> TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));</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>  <span class="comment">// Set up TRANSPOSE_CONV2D operator</span></div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">// The TOSA Reference Model pads the output shape, so it is added to output shape.</span></div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// In Arm NN we pad the input shape, so it is taken away.</span></div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// To offset this the negative padding value can be used.</span></div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::vector<int> pad = {-<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>),</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  -<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>),</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  -<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>),</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  -<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>)};</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  std::vector<int> stride = {<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>),</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</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>  std::vector<int> outputShape;</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// If available use shape in descriptor otherwise use output shape.</span></div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">if</span> (descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a78f5b213a603b55d0fabea389e89f203">m_OutputShape</a>.size() == 4)</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  {</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a78f5b213a603b55d0fabea389e89f203">m_OutputShape</a>.size(); ++i)</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  {</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  outputShape.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(descriptor-><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a78f5b213a603b55d0fabea389e89f203">m_OutputShape</a>[i]));</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  }</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  {</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < outputs[0]->GetNumDimensions(); ++i)</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>  outputShape.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(outputs[0]->GetShape()[i]));</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>  }</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>  TosaTransposeConvAttribute attribute(pad, stride, outputShape, 0, 0, <span class="keyword">false</span>); <span class="comment">// input_zp, weight_zp, local_bound</span></div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  </div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">auto</span>* op = <span class="keyword">new</span> TosaSerializationOperator(Op_TRANSPOSE_CONV2D,</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  Attribute_TransposeConvAttribute,</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  &attribute,</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  {input0Name, input1Name, input2Name},</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  {outputName});</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  operators.push_back(op);</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  </div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="comment">// operatorInputNames/operatorOutputNames ends up being the same as</span></div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings</span></div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> TosaSerializationBasicBlock(blockName, <span class="comment">// name</span></div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="_tosa_operator_utils_8hpp.html#a28514b014a4fe0841044f1868064bc65">mainName</a>, <span class="comment">// region name</span></div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  operators, <span class="comment">// operators</span></div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  tensors, <span class="comment">// tensors</span></div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  {input0Name, input1Name, input2Name}, <span class="comment">// inputs</span></div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  {outputName}); <span class="comment">// outputs</span></div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> }</div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01469">Descriptors.hpp:1469</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01477">Descriptors.hpp:1477</a></div></div>
-<div class="ttc" id="a_tosa_operator_utils_8hpp_html_ae03fdd5482e75faa3b846bd021678f3b"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#ae03fdd5482e75faa3b846bd021678f3b">ConvertConstantTensorDataToBuffer</a></div><div class="ttdeci">std::vector< uint8_t > ConvertConstantTensorDataToBuffer(const std::shared_ptr< ConstTensorHandle > &tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.html#l00289">TosaOperatorUtils.hpp:289</a></div></div>
+<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a1e5b1f8dcc21f10bc7b0d8517e05049d"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#a1e5b1f8dcc21f10bc7b0d8517e05049d">GenerateUniqueOutputName</a></div><div class="ttdeci">std::string GenerateUniqueOutputName(const Layer &layer, uint32_t layerSlot=0)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.html#l00120">TosaOperatorUtils.hpp:120</a></div></div>
+<div class="ttc" id="a_tosa_operator_utils_8hpp_html_ae03fdd5482e75faa3b846bd021678f3b"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#ae03fdd5482e75faa3b846bd021678f3b">ConvertConstantTensorDataToBuffer</a></div><div class="ttdeci">std::vector< uint8_t > ConvertConstantTensorDataToBuffer(const std::shared_ptr< ConstTensorHandle > &tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.html#l00333">TosaOperatorUtils.hpp:333</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.html#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.html#l00337">Layer.hpp:337</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">armnn::TransposeConvolution2dDescriptor::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.html#l01475">Descriptors.hpp:1475</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html"><div class="ttname"><a href="classarmnn_1_1_layer.html">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00230">Layer.hpp:230</a></div></div>
<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a28514b014a4fe0841044f1868064bc65"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#a28514b014a4fe0841044f1868064bc65">mainName</a></div><div class="ttdeci">const std::string mainName</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.html#l00019">TosaOperatorUtils.hpp:19</a></div></div>
-<div class="ttc" id="aclassarmnn_1_1_output_slot_html_a7ddaf04177053a536f0e7be83a642bc6"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">armnn::OutputSlot::GetOwningLayer</a></div><div class="ttdeci">Layer & GetOwningLayer() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00132">Layer.hpp:132</a></div></div>
<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a45d66f17ad6b0469e469f443b3e03226"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#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.html#l00022">TosaOperatorUtils.hpp:22</a></div></div>
-<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a246662c69dac647833be50ba6dcee024"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#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.html#l00082">TosaOperatorUtils.hpp:82</a></div></div>
<div class="ttc" id="a_transpose_convolution2d_layer_8hpp_html"><div class="ttname"><a href="_transpose_convolution2d_layer_8hpp.html">TransposeConvolution2dLayer.hpp</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01479">Descriptors.hpp:1479</a></div></div>
<div class="ttc" id="a_transpose_conv2d_operator_8cpp_html_a6d30ef27a71abb69a0bb31d398759fda"><div class="ttname"><a href="_transpose_conv2d_operator_8cpp.html#a6d30ef27a71abb69a0bb31d398759fda">ConvertTransposeConv2dToTosaOperator</a></div><div class="ttdeci">TosaSerializationBasicBlock * ConvertTransposeConv2dToTosaOperator(const Layer *layer, const std::vector< const TensorInfo * > &inputs, const std::vector< const TensorInfo * > &outputs, const TransposeConvolution2dDescriptor *descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_transpose_conv2d_operator_8cpp_source.html#l00010">TransposeConv2dOperator.cpp:10</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_a78f5b213a603b55d0fabea389e89f203"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a78f5b213a603b55d0fabea389e89f203">armnn::TransposeConvolution2dDescriptor::m_OutputShape</a></div><div class="ttdeci">std::vector< unsigned int > m_OutputShape</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01486">Descriptors.hpp:1486</a></div></div>
-<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a1640c964a461e8580837a79829a5c197"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#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.html#l00063">TosaOperatorUtils.hpp:63</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">armnn::TransposeConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01473">Descriptors.hpp:1473</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a002bc30e590d78cbb4f4d12171055ca7">armnn::TransposeConvolution2dDescriptor::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.html#l01471">Descriptors.hpp:1471</a></div></div>
<div class="ttc" id="a_transpose_conv2d_operator_8hpp_html"><div class="ttname"><a href="_transpose_conv2d_operator_8hpp.html">TransposeConv2dOperator.hpp</a></div></div>
-<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a7d97964a65be4eb4a5b4109904f2e7f7"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#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.html#l00052">TosaOperatorUtils.hpp:52</a></div></div>
-<div class="ttc" id="aclassarmnn_1_1_input_slot_html_a9effd325a6d512a3f8ff4bd207d53255"><div class="ttname"><a href="classarmnn_1_1_input_slot.html#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.html#l00056">Layer.hpp:56</a></div></div>
+<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a7d97964a65be4eb4a5b4109904f2e7f7"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#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.html#l00079">TosaOperatorUtils.hpp:79</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01440">Descriptors.hpp:1440</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01481">Descriptors.hpp:1481</a></div></div>
<div class="ttc" id="astructarmnn_1_1_transpose_convolution2d_descriptor_html_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">armnn::TransposeConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01483">Descriptors.hpp:1483</a></div></div>
-<div class="ttc" id="a_tosa_operator_utils_8hpp_html_aadc5d73bd0cb81999bcfdc62bce020e8"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#aadc5d73bd0cb81999bcfdc62bce020e8">GetUniqueTosaMappingID</a></div><div class="ttdeci">std::string GetUniqueTosaMappingID()</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.html#l00100">TosaOperatorUtils.hpp:100</a></div></div>
+<div class="ttc" id="a_tosa_operator_utils_8hpp_html_a8863b57c08a748fd2dd16880337f4b69"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#a8863b57c08a748fd2dd16880337f4b69">GenerateUniqueInputName</a></div><div class="ttdeci">std::string GenerateUniqueInputName(const armnn::InputSlot &slot)</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.html#l00109">TosaOperatorUtils.hpp:109</a></div></div>
+<div class="ttc" id="a_tosa_operator_utils_8hpp_html_aadc5d73bd0cb81999bcfdc62bce020e8"><div class="ttname"><a href="_tosa_operator_utils_8hpp.html#aadc5d73bd0cb81999bcfdc62bce020e8">GetUniqueTosaMappingID</a></div><div class="ttdeci">std::string GetUniqueTosaMappingID()</div><div class="ttdef"><b>Definition:</b> <a href="_tosa_operator_utils_8hpp_source.html#l00138">TosaOperatorUtils.hpp:138</a></div></div>
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