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96<a href="#nested-classes">Classes</a> &#124;
97<a href="#func-members">Functions</a> </div>
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99<div class="title">armnn::optimizations::pad_fold Namespace Reference</div> </div>
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101<div class="contents">
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103<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
104Classes</h2></td></tr>
105<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_convolution2d_impl.html">FoldPadIntoConvolution2dImpl</a></td></tr>
106<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
107<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_depthwise_convolution2d_impl.html">FoldPadIntoDepthwiseConvolution2dImpl</a></td></tr>
108<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
109<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1optimizations_1_1pad__fold_1_1_fold_pad_into_pooling2d_impl.html">FoldPadIntoPooling2dImpl</a></td></tr>
110<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
111</table><table class="memberdecls">
112<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
113Functions</h2></td></tr>
114<tr class="memitem:a41605a45fe3f148071b04c7d861f391f"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a> (const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;tensorInfo)</td></tr>
115<tr class="separator:a41605a45fe3f148071b04c7d861f391f"><td class="memSeparator" colspan="2">&#160;</td></tr>
116<tr class="memitem:a1112c7c010be092e8d2478e5268666de"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a1112c7c010be092e8d2478e5268666de">GetLowestElement</a> (const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;tensorInfo)</td></tr>
117<tr class="separator:a1112c7c010be092e8d2478e5268666de"><td class="memSeparator" colspan="2">&#160;</td></tr>
118<tr class="memitem:aef8fbdfbe08862db57b8ea6e09d84bce"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#aef8fbdfbe08862db57b8ea6e09d84bce">IsNeutralElement</a> (const <a class="el" href="structarmnn_1_1_convolution2d_descriptor.html">Convolution2dDescriptor</a> &amp;, const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;tensorInfo, const float tensorValue)</td></tr>
119<tr class="separator:aef8fbdfbe08862db57b8ea6e09d84bce"><td class="memSeparator" colspan="2">&#160;</td></tr>
120<tr class="memitem:a9c5795e478ba9afc068c645f3ac72ca5"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a9c5795e478ba9afc068c645f3ac72ca5">IsNeutralElement</a> (const <a class="el" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">DepthwiseConvolution2dDescriptor</a> &amp;, const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;tensorInfo, const float tensorValue)</td></tr>
121<tr class="separator:a9c5795e478ba9afc068c645f3ac72ca5"><td class="memSeparator" colspan="2">&#160;</td></tr>
122<tr class="memitem:a860dc7bc83a72db266ef5d6759686d24"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a860dc7bc83a72db266ef5d6759686d24">IsNeutralElement</a> (const <a class="el" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> &amp;descriptor, const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;tensorInfo, const float tensorValue)</td></tr>
123<tr class="separator:a860dc7bc83a72db266ef5d6759686d24"><td class="memSeparator" colspan="2">&#160;</td></tr>
124<tr class="memitem:a9f3ad988ab5cf0c11de5380e77bbb50e"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a9f3ad988ab5cf0c11de5380e77bbb50e">IsPooling2dPadded</a> (const <a class="el" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> &amp;poolDescriptor)</td></tr>
125<tr class="separator:a9f3ad988ab5cf0c11de5380e77bbb50e"><td class="memSeparator" colspan="2">&#160;</td></tr>
126<tr class="memitem:a33ffc65d1f6581b0789d3d3a033f698e"><td class="memTemplParams" colspan="2">template&lt;typename Descriptor &gt; </td></tr>
127<tr class="memitem:a33ffc65d1f6581b0789d3d3a033f698e"><td class="memTemplItemLeft" align="right" valign="top">bool&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a33ffc65d1f6581b0789d3d3a033f698e">TryFoldPadIntoLayer2d</a> (const <a class="el" href="structarmnn_1_1_pad_descriptor.html">PadDescriptor</a> &amp;padDescriptor, Descriptor &amp;layerDescriptor, const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;tensorInfo)</td></tr>
128<tr class="separator:a33ffc65d1f6581b0789d3d3a033f698e"><td class="memSeparator" colspan="2">&#160;</td></tr>
129<tr class="memitem:adeaaacf15ed6830d77298930545187e6"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#adeaaacf15ed6830d77298930545187e6">TryFoldPadIntoLayer2d</a> (const <a class="el" href="structarmnn_1_1_pad_descriptor.html">PadDescriptor</a> &amp;padDescriptor, <a class="el" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> &amp;poolDescriptor, const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;tensorInfo, bool isBackendOptimization=false)</td></tr>
130<tr class="separator:adeaaacf15ed6830d77298930545187e6"><td class="memSeparator" colspan="2">&#160;</td></tr>
131<tr class="memitem:a0dfb192db7209941d02bba0bd904822e"><td class="memTemplParams" colspan="2">template&lt;typename Layer2dT &gt; </td></tr>
132<tr class="memitem:a0dfb192db7209941d02bba0bd904822e"><td class="memTemplItemLeft" align="right" valign="top">Layer2dT *&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a0dfb192db7209941d02bba0bd904822e">FoldPadIntoLayer2dImpl</a> (<a class="el" href="classarmnn_1_1_graph.html">Graph</a> &amp;graph, <a class="el" href="classarmnn_1_1_input_slot.html">InputSlot</a> &amp;connection)</td></tr>
133<tr class="separator:a0dfb192db7209941d02bba0bd904822e"><td class="memSeparator" colspan="2">&#160;</td></tr>
134</table>
135<h2 class="groupheader">Function Documentation</h2>
136<a id="a0dfb192db7209941d02bba0bd904822e"></a>
137<h2 class="memtitle"><span class="permalink"><a href="#a0dfb192db7209941d02bba0bd904822e">&#9670;&nbsp;</a></span>FoldPadIntoLayer2dImpl()</h2>
138
139<div class="memitem">
140<div class="memproto">
141 <table class="memname">
142 <tr>
143 <td class="memname">Layer2dT* armnn::optimizations::pad_fold::FoldPadIntoLayer2dImpl </td>
144 <td>(</td>
145 <td class="paramtype"><a class="el" href="classarmnn_1_1_graph.html">Graph</a> &amp;&#160;</td>
146 <td class="paramname"><em>graph</em>, </td>
147 </tr>
148 <tr>
149 <td class="paramkey"></td>
150 <td></td>
151 <td class="paramtype"><a class="el" href="classarmnn_1_1_input_slot.html">InputSlot</a> &amp;&#160;</td>
152 <td class="paramname"><em>connection</em>&#160;</td>
153 </tr>
154 <tr>
155 <td></td>
156 <td>)</td>
157 <td></td><td></td>
158 </tr>
159 </table>
160</div><div class="memdoc">
161
162<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00144">144</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
163<div class="fragment"><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;{</div>
164<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; PadLayer&amp; padLayer = *PolymorphicDowncast&lt;PadLayer*&gt;(&amp;connection.GetConnectedOutputSlot()-&gt;GetOwningLayer());</div>
165<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; Layer2dT&amp; layer2d = *PolymorphicDowncast&lt;Layer2dT*&gt;(&amp;connection.GetOwningLayer());</div>
166<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; </div>
167<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; <span class="keyword">const</span> PadDescriptor&amp; padDescriptor = padLayer.GetParameters();</div>
168<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="keyword">auto</span> newLayer2dDescriptor = layer2d.GetParameters();</div>
169<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; </div>
170<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <span class="keywordflow">if</span> (!<a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#adeaaacf15ed6830d77298930545187e6">TryFoldPadIntoLayer2d</a>(padDescriptor, newLayer2dDescriptor, padLayer.GetOutputSlot().GetTensorInfo()))</div>
171<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; {</div>
172<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div>
173<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; }</div>
174<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; </div>
175<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; <span class="comment">// Workaround an issue in the compute library. The conv2d algorithm that the</span></div>
176<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="comment">// compute library is choosing is not handling the 1x1 filter case when</span></div>
177<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; <span class="comment">// the padding size &gt;= filter size</span></div>
178<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <span class="keywordflow">if</span> (layer2d.GetType() == <a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a>)</div>
179<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; {</div>
180<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="comment">// Get filter width and height</span></div>
181<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html">armnnUtils::DataLayoutIndexed</a> dataLayoutIndex(newLayer2dDescriptor.m_DataLayout);</div>
182<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keyword">const</span> TensorShape&amp; filterShape = layer2d.GetInputSlot(1).GetTensorInfo().GetShape();</div>
183<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];</div>
184<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];</div>
185<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <span class="comment">// Calculate total padding and check conditions</span></div>
186<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <span class="keyword">auto</span> horizontalPadding = newLayer2dDescriptor.m_PadLeft + newLayer2dDescriptor.m_PadRight;</div>
187<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; <span class="keyword">auto</span> verticalPadding = newLayer2dDescriptor.m_PadTop + newLayer2dDescriptor.m_PadBottom;</div>
188<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; <span class="keywordflow">if</span> ((filterWidth == 1) &amp;&amp; (horizontalPadding &gt;= filterWidth))</div>
189<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; {</div>
190<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div>
191<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; }</div>
192<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; <span class="keywordflow">else</span> <span class="keywordflow">if</span> ((filterHeight == 1) &amp;&amp; (verticalPadding &gt;= filterHeight))</div>
193<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; {</div>
194<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div>
195<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; }</div>
196<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; }</div>
197<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; </div>
198<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <span class="comment">// Save original parent output slot of the pad layer</span></div>
199<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; OutputSlot&amp; parentSlot = *padLayer.GetInputSlot(0).GetConnectedOutputSlot();</div>
200<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; </div>
201<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <span class="comment">// Insert new layer2d layer between the pad layer and its parent layer.</span></div>
202<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <span class="keyword">const</span> std::string name = std::string(<span class="stringliteral">&quot;folded-&quot;</span>) + padLayer.GetName() + <span class="stringliteral">&quot;-into-&quot;</span> + layer2d.GetName();</div>
203<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <span class="keyword">auto</span>&amp; newLayer2d = *graph.InsertNewLayer&lt;Layer2dT&gt;(padLayer.GetInputSlot(0), newLayer2dDescriptor, name.c_str());</div>
204<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; </div>
205<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; newLayer2d.GetOutputSlot().MoveAllConnections(parentSlot);</div>
206<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <span class="comment">// Start at 1 to connect only weights and bias</span></div>
207<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1; i &lt; layer2d.GetNumInputSlots(); ++i)</div>
208<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; {</div>
209<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; <span class="keywordflow">if</span> (layer2d.GetInputSlot(i).GetConnectedOutputSlot() != <span class="keyword">nullptr</span>)</div>
210<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; {</div>
211<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; Layer&amp; tgtLayer = layer2d.GetInputSlot(i).GetConnectedOutputSlot()-&gt;GetOwningLayer();</div>
212<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; <span class="comment">// Remove old connection and connect to new layer2d</span></div>
213<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; tgtLayer.GetOutputSlot(0).Disconnect(layer2d.GetInputSlot(i));</div>
214<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; tgtLayer.GetOutputSlot(0).Connect(newLayer2d.GetInputSlot(i));</div>
215<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; }</div>
216<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; }</div>
217<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; </div>
218<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="comment">// Moves connections in old layer2d layer output to new layer.</span></div>
219<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <span class="comment">// Old layer2d layer will be removed as it&#39;s left unconnected.</span></div>
220<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <span class="comment">// Pad layer will be removed if left unconnected.</span></div>
221<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; layer2d.GetOutputSlot().MoveAllConnections(newLayer2d.GetOutputSlot());</div>
222<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; </div>
223<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; <span class="keywordflow">return</span> &amp;newLayer2d;</div>
224<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160;}</div>
225</div><!-- fragment -->
226<p class="reference">References <a class="el" href="_layer_8cpp_source.html#l00123">OutputSlot::Connect()</a>, <a class="el" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::Convolution2d</a>, <a class="el" href="_layer_8cpp_source.html#l00131">OutputSlot::Disconnect()</a>, <a class="el" href="_layer_8hpp_source.html#l00056">InputSlot::GetConnectedOutputSlot()</a>, <a class="el" href="_data_layout_indexed_8hpp_source.html#l00024">DataLayoutIndexed::GetHeightIndex()</a>, <a class="el" href="_layer_8hpp_source.html#l00337">Layer::GetInputSlot()</a>, <a class="el" href="_layer_8hpp_source.html#l00332">Layer::GetName()</a>, <a class="el" href="_layer_8hpp_source.html#l00339">Layer::GetOutputSlot()</a>, <a class="el" href="_layer_8hpp_source.html#l00053">InputSlot::GetOwningLayer()</a>, <a class="el" href="_layer_8hpp_source.html#l00132">OutputSlot::GetOwningLayer()</a>, <a class="el" href="_layer_with_parameters_8hpp_source.html#l00019">LayerWithParameters&lt; Parameters &gt;::GetParameters()</a>, <a class="el" href="_layer_8cpp_source.html#l00100">OutputSlot::GetTensorInfo()</a>, <a class="el" href="_data_layout_indexed_8hpp_source.html#l00025">DataLayoutIndexed::GetWidthIndex()</a>, <a class="el" href="_graph_8hpp_source.html#l00481">Graph::InsertNewLayer()</a>, and <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00088">TryFoldPadIntoLayer2d()</a>.</p>
227
228</div>
229</div>
230<a id="a1112c7c010be092e8d2478e5268666de"></a>
231<h2 class="memtitle"><span class="permalink"><a href="#a1112c7c010be092e8d2478e5268666de">&#9670;&nbsp;</a></span>GetLowestElement()</h2>
232
233<div class="memitem">
234<div class="memproto">
235<table class="mlabels">
236 <tr>
237 <td class="mlabels-left">
238 <table class="memname">
239 <tr>
240 <td class="memname">float armnn::optimizations::pad_fold::GetLowestElement </td>
241 <td>(</td>
242 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;&#160;</td>
243 <td class="paramname"><em>tensorInfo</em></td><td>)</td>
244 <td></td>
245 </tr>
246 </table>
247 </td>
248 <td class="mlabels-right">
249<span class="mlabels"><span class="mlabel">inline</span></span> </td>
250 </tr>
251</table>
252</div><div class="memdoc">
253
254<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00026">26</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
255<div class="fragment"><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;{</div>
256<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; constexpr <span class="keywordtype">float</span> negativeInfinity = -std::numeric_limits&lt;float&gt;::infinity();</div>
257<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> scale = tensorInfo.GetQuantizationScale();</div>
258<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; <span class="keyword">const</span> int32_t offset = tensorInfo.GetQuantizationOffset();</div>
259<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; </div>
260<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keywordflow">switch</span> (tensorInfo.GetDataType())</div>
261<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; {</div>
262<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; <span class="keywordflow">case</span> DataType::Float16:</div>
263<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize&lt;armnn::Half&gt;(negativeInfinity, scale, offset);</div>
264<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="keywordflow">case</span> DataType::Float32:</div>
265<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize&lt;float&gt;(negativeInfinity, scale, offset);</div>
266<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; <span class="keywordflow">case</span> DataType::QAsymmU8:</div>
267<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize&lt;uint8_t&gt;(negativeInfinity, scale, offset);</div>
268<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="keywordflow">case</span> DataType::QSymmS16:</div>
269<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize&lt;int16_t&gt;(negativeInfinity, scale, offset);</div>
270<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="keywordflow">case</span> DataType::QSymmS8:</div>
271<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="comment">// Fall-through</span></div>
272<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <span class="keywordflow">case</span> DataType::QAsymmS8:</div>
273<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize&lt;int8_t&gt;(negativeInfinity, scale, offset);</div>
274<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; <span class="keywordflow">case</span> DataType::BFloat16:</div>
275<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <span class="keywordflow">return</span> armnnUtils::SelectiveQuantize&lt;armnn::BFloat16&gt;(negativeInfinity, scale, offset);</div>
276<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="keywordflow">default</span>:</div>
277<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; {</div>
278<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <a class="code" href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">&quot;Unsupported DataType&quot;</span>);</div>
279<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keywordflow">return</span> NAN;</div>
280<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; }</div>
281<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; }</div>
282<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;}</div>
283</div><!-- fragment -->
284<p class="reference">References <a class="el" href="_assert_8hpp_source.html#l00015">ARMNN_ASSERT_MSG</a>, <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::BFloat16</a>, <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::Float16</a>, <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_tensor_8hpp_source.html#l00200">TensorInfo::GetDataType()</a>, <a class="el" href="_tensor_8cpp_source.html#l00482">TensorInfo::GetQuantizationOffset()</a>, <a class="el" href="_tensor_8cpp_source.html#l00461">TensorInfo::GetQuantizationScale()</a>, <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::QAsymmS8</a>, <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>, <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::QSymmS16</a>, and <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::QSymmS8</a>.</p>
285
286<p class="reference">Referenced by <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00068">IsNeutralElement()</a>.</p>
287
288</div>
289</div>
290<a id="a41605a45fe3f148071b04c7d861f391f"></a>
291<h2 class="memtitle"><span class="permalink"><a href="#a41605a45fe3f148071b04c7d861f391f">&#9670;&nbsp;</a></span>GetZeroElement()</h2>
292
293<div class="memitem">
294<div class="memproto">
295<table class="mlabels">
296 <tr>
297 <td class="mlabels-left">
298 <table class="memname">
299 <tr>
300 <td class="memname">float armnn::optimizations::pad_fold::GetZeroElement </td>
301 <td>(</td>
302 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;&#160;</td>
303 <td class="paramname"><em>tensorInfo</em></td><td>)</td>
304 <td></td>
305 </tr>
306 </table>
307 </td>
308 <td class="mlabels-right">
309<span class="mlabels"><span class="mlabel">inline</span></span> </td>
310 </tr>
311</table>
312</div><div class="memdoc">
313
314<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00021">21</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
315<div class="fragment"><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;{</div>
316<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; <span class="keywordflow">return</span> <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(tensorInfo.IsQuantized() ? tensorInfo.GetQuantizationOffset() : 0);</div>
317<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;}</div>
318</div><!-- fragment -->
319<p class="reference">References <a class="el" href="_tensor_8cpp_source.html#l00482">TensorInfo::GetQuantizationOffset()</a>, and <a class="el" href="_tensor_8cpp_source.html#l00508">TensorInfo::IsQuantized()</a>.</p>
320
321<p class="reference">Referenced by <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00056">IsNeutralElement()</a>.</p>
322
323</div>
324</div>
325<a id="aef8fbdfbe08862db57b8ea6e09d84bce"></a>
326<h2 class="memtitle"><span class="permalink"><a href="#aef8fbdfbe08862db57b8ea6e09d84bce">&#9670;&nbsp;</a></span>IsNeutralElement() <span class="overload">[1/3]</span></h2>
327
328<div class="memitem">
329<div class="memproto">
330<table class="mlabels">
331 <tr>
332 <td class="mlabels-left">
333 <table class="memname">
334 <tr>
335 <td class="memname">bool armnn::optimizations::pad_fold::IsNeutralElement </td>
336 <td>(</td>
337 <td class="paramtype">const <a class="el" href="structarmnn_1_1_convolution2d_descriptor.html">Convolution2dDescriptor</a> &amp;&#160;</td>
338 <td class="paramname">, </td>
339 </tr>
340 <tr>
341 <td class="paramkey"></td>
342 <td></td>
343 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;&#160;</td>
344 <td class="paramname"><em>tensorInfo</em>, </td>
345 </tr>
346 <tr>
347 <td class="paramkey"></td>
348 <td></td>
349 <td class="paramtype">const float&#160;</td>
350 <td class="paramname"><em>tensorValue</em>&#160;</td>
351 </tr>
352 <tr>
353 <td></td>
354 <td>)</td>
355 <td></td><td></td>
356 </tr>
357 </table>
358 </td>
359 <td class="mlabels-right">
360<span class="mlabels"><span class="mlabel">inline</span></span> </td>
361 </tr>
362</table>
363</div><div class="memdoc">
364
365<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00056">56</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
366<div class="fragment"><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;{</div>
367<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="keywordflow">return</span> tensorValue == <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a>(tensorInfo);</div>
368<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;}</div>
369</div><!-- fragment -->
370<p class="reference">References <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00021">GetZeroElement()</a>.</p>
371
372<p class="reference">Referenced by <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00088">TryFoldPadIntoLayer2d()</a>.</p>
373
374</div>
375</div>
376<a id="a9c5795e478ba9afc068c645f3ac72ca5"></a>
377<h2 class="memtitle"><span class="permalink"><a href="#a9c5795e478ba9afc068c645f3ac72ca5">&#9670;&nbsp;</a></span>IsNeutralElement() <span class="overload">[2/3]</span></h2>
378
379<div class="memitem">
380<div class="memproto">
381<table class="mlabels">
382 <tr>
383 <td class="mlabels-left">
384 <table class="memname">
385 <tr>
386 <td class="memname">bool armnn::optimizations::pad_fold::IsNeutralElement </td>
387 <td>(</td>
388 <td class="paramtype">const <a class="el" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">DepthwiseConvolution2dDescriptor</a> &amp;&#160;</td>
389 <td class="paramname">, </td>
390 </tr>
391 <tr>
392 <td class="paramkey"></td>
393 <td></td>
394 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;&#160;</td>
395 <td class="paramname"><em>tensorInfo</em>, </td>
396 </tr>
397 <tr>
398 <td class="paramkey"></td>
399 <td></td>
400 <td class="paramtype">const float&#160;</td>
401 <td class="paramname"><em>tensorValue</em>&#160;</td>
402 </tr>
403 <tr>
404 <td></td>
405 <td>)</td>
406 <td></td><td></td>
407 </tr>
408 </table>
409 </td>
410 <td class="mlabels-right">
411<span class="mlabels"><span class="mlabel">inline</span></span> </td>
412 </tr>
413</table>
414</div><div class="memdoc">
415
416<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00061">61</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
417<div class="fragment"><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;{</div>
418<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="keywordflow">return</span> tensorValue == <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a>(tensorInfo);</div>
419<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;}</div>
420</div><!-- fragment -->
421<p class="reference">References <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00021">GetZeroElement()</a>.</p>
422
423</div>
424</div>
425<a id="a860dc7bc83a72db266ef5d6759686d24"></a>
426<h2 class="memtitle"><span class="permalink"><a href="#a860dc7bc83a72db266ef5d6759686d24">&#9670;&nbsp;</a></span>IsNeutralElement() <span class="overload">[3/3]</span></h2>
427
428<div class="memitem">
429<div class="memproto">
430<table class="mlabels">
431 <tr>
432 <td class="mlabels-left">
433 <table class="memname">
434 <tr>
435 <td class="memname">bool armnn::optimizations::pad_fold::IsNeutralElement </td>
436 <td>(</td>
437 <td class="paramtype">const <a class="el" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> &amp;&#160;</td>
438 <td class="paramname"><em>descriptor</em>, </td>
439 </tr>
440 <tr>
441 <td class="paramkey"></td>
442 <td></td>
443 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;&#160;</td>
444 <td class="paramname"><em>tensorInfo</em>, </td>
445 </tr>
446 <tr>
447 <td class="paramkey"></td>
448 <td></td>
449 <td class="paramtype">const float&#160;</td>
450 <td class="paramname"><em>tensorValue</em>&#160;</td>
451 </tr>
452 <tr>
453 <td></td>
454 <td>)</td>
455 <td></td><td></td>
456 </tr>
457 </table>
458 </td>
459 <td class="mlabels-right">
460<span class="mlabels"><span class="mlabel">inline</span></span> </td>
461 </tr>
462</table>
463</div><div class="memdoc">
464
465<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00068">68</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
466<div class="fragment"><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;{</div>
467<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; <span class="keywordflow">return</span> (descriptor.m_PoolType == PoolingAlgorithm::Max)</div>
468<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; ? tensorValue &lt;= <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a1112c7c010be092e8d2478e5268666de">GetLowestElement</a>(tensorInfo)</div>
469<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; : tensorValue == <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a41605a45fe3f148071b04c7d861f391f">GetZeroElement</a>(tensorInfo);</div>
470<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;}</div>
471</div><!-- fragment -->
472<p class="reference">References <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00026">GetLowestElement()</a>, <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00021">GetZeroElement()</a>, <a class="el" href="_descriptors_8hpp_source.html#l00405">Pooling2dDescriptor::m_PoolType</a>, and <a class="el" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::Max</a>.</p>
473
474</div>
475</div>
476<a id="a9f3ad988ab5cf0c11de5380e77bbb50e"></a>
477<h2 class="memtitle"><span class="permalink"><a href="#a9f3ad988ab5cf0c11de5380e77bbb50e">&#9670;&nbsp;</a></span>IsPooling2dPadded()</h2>
478
479<div class="memitem">
480<div class="memproto">
481<table class="mlabels">
482 <tr>
483 <td class="mlabels-left">
484 <table class="memname">
485 <tr>
486 <td class="memname">bool armnn::optimizations::pad_fold::IsPooling2dPadded </td>
487 <td>(</td>
488 <td class="paramtype">const <a class="el" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> &amp;&#160;</td>
489 <td class="paramname"><em>poolDescriptor</em></td><td>)</td>
490 <td></td>
491 </tr>
492 </table>
493 </td>
494 <td class="mlabels-right">
495<span class="mlabels"><span class="mlabel">inline</span></span> </td>
496 </tr>
497</table>
498</div><div class="memdoc">
499
500<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00076">76</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
501<div class="fragment"><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;{</div>
502<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keyword">const</span> <span class="keyword">auto</span> poolingPadValues = std::make_tuple(poolDescriptor.m_PadLeft, poolDescriptor.m_PadRight,</div>
503<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; poolDescriptor.m_PadTop, poolDescriptor.m_PadBottom);</div>
504<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="keywordflow">if</span> (poolingPadValues != std::make_tuple(0U, 0U, 0U, 0U))</div>
505<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; {</div>
506<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
507<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; }</div>
508<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
509<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;}</div>
510</div><!-- fragment -->
511<p class="reference">References <a class="el" href="_descriptors_8hpp_source.html#l00413">Pooling2dDescriptor::m_PadBottom</a>, <a class="el" href="_descriptors_8hpp_source.html#l00407">Pooling2dDescriptor::m_PadLeft</a>, <a class="el" href="_descriptors_8hpp_source.html#l00409">Pooling2dDescriptor::m_PadRight</a>, and <a class="el" href="_descriptors_8hpp_source.html#l00411">Pooling2dDescriptor::m_PadTop</a>.</p>
512
513<p class="reference">Referenced by <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00115">TryFoldPadIntoLayer2d()</a>.</p>
514
515</div>
516</div>
517<a id="a33ffc65d1f6581b0789d3d3a033f698e"></a>
518<h2 class="memtitle"><span class="permalink"><a href="#a33ffc65d1f6581b0789d3d3a033f698e">&#9670;&nbsp;</a></span>TryFoldPadIntoLayer2d() <span class="overload">[1/2]</span></h2>
519
520<div class="memitem">
521<div class="memproto">
522 <table class="memname">
523 <tr>
524 <td class="memname">bool armnn::optimizations::pad_fold::TryFoldPadIntoLayer2d </td>
525 <td>(</td>
526 <td class="paramtype">const <a class="el" href="structarmnn_1_1_pad_descriptor.html">PadDescriptor</a> &amp;&#160;</td>
527 <td class="paramname"><em>padDescriptor</em>, </td>
528 </tr>
529 <tr>
530 <td class="paramkey"></td>
531 <td></td>
532 <td class="paramtype">Descriptor &amp;&#160;</td>
533 <td class="paramname"><em>layerDescriptor</em>, </td>
534 </tr>
535 <tr>
536 <td class="paramkey"></td>
537 <td></td>
538 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;&#160;</td>
539 <td class="paramname"><em>tensorInfo</em>&#160;</td>
540 </tr>
541 <tr>
542 <td></td>
543 <td>)</td>
544 <td></td><td></td>
545 </tr>
546 </table>
547</div><div class="memdoc">
548
549<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00088">88</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
550<div class="fragment"><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;{</div>
551<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html">armnnUtils::DataLayoutIndexed</a> layout = <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html">armnnUtils::DataLayoutIndexed</a>(layerDescriptor.m_DataLayout);</div>
552<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchIndex = 0;</div>
553<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; </div>
554<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; constexpr <span class="keyword">auto</span> noPad = std::make_pair(0U, 0U);</div>
555<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; </div>
556<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="keywordflow">if</span> ((!<a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a860dc7bc83a72db266ef5d6759686d24">IsNeutralElement</a>(layerDescriptor, tensorInfo, padDescriptor.m_PadValue)) ||</div>
557<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; (padDescriptor.m_PadList[batchIndex] != noPad) || (padDescriptor.m_PadList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()] != noPad))</div>
558<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; {</div>
559<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
560<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; }</div>
561<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; </div>
562<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="keyword">const</span> <span class="keyword">auto</span>&amp; padList = padDescriptor.m_PadList;</div>
563<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; </div>
564<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="comment">// In Convolution2dDescriptor/Pooling2dDescriptor, padLeft and padRight are defined as paddings</span></div>
565<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="comment">// on width dimension whereas padTop and padBottom - paddings on height dimension, so updating</span></div>
566<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="comment">// these according to data layout</span></div>
567<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; layerDescriptor.m_PadLeft += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()].first;</div>
568<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; layerDescriptor.m_PadRight += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()].second;</div>
569<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; layerDescriptor.m_PadTop += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()].first;</div>
570<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; layerDescriptor.m_PadBottom += padList[layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()].second;</div>
571<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; </div>
572<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
573<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160;}</div>
574</div><!-- fragment -->
575<p class="reference">References <a class="el" href="_data_layout_indexed_8hpp_source.html#l00023">DataLayoutIndexed::GetChannelsIndex()</a>, <a class="el" href="_data_layout_indexed_8hpp_source.html#l00024">DataLayoutIndexed::GetHeightIndex()</a>, <a class="el" href="_data_layout_indexed_8hpp_source.html#l00025">DataLayoutIndexed::GetWidthIndex()</a>, <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00056">IsNeutralElement()</a>, <a class="el" href="_descriptors_8hpp_source.html#l01218">PadDescriptor::m_PadList</a>, and <a class="el" href="_descriptors_8hpp_source.html#l01221">PadDescriptor::m_PadValue</a>.</p>
576
577<p class="reference">Referenced by <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00144">FoldPadIntoLayer2dImpl()</a>, <a class="el" href="_ref_backend_8cpp_source.html#l00072">RefBackend::OptimizeSubgraphView()</a>, and <a class="el" href="_cl_backend_8cpp_source.html#l00292">ClBackend::OptimizeSubgraphView()</a>.</p>
578
579</div>
580</div>
581<a id="adeaaacf15ed6830d77298930545187e6"></a>
582<h2 class="memtitle"><span class="permalink"><a href="#adeaaacf15ed6830d77298930545187e6">&#9670;&nbsp;</a></span>TryFoldPadIntoLayer2d() <span class="overload">[2/2]</span></h2>
583
584<div class="memitem">
585<div class="memproto">
586<table class="mlabels">
587 <tr>
588 <td class="mlabels-left">
589 <table class="memname">
590 <tr>
591 <td class="memname">bool armnn::optimizations::pad_fold::TryFoldPadIntoLayer2d </td>
592 <td>(</td>
593 <td class="paramtype">const <a class="el" href="structarmnn_1_1_pad_descriptor.html">PadDescriptor</a> &amp;&#160;</td>
594 <td class="paramname"><em>padDescriptor</em>, </td>
595 </tr>
596 <tr>
597 <td class="paramkey"></td>
598 <td></td>
599 <td class="paramtype"><a class="el" href="structarmnn_1_1_pooling2d_descriptor.html">Pooling2dDescriptor</a> &amp;&#160;</td>
600 <td class="paramname"><em>poolDescriptor</em>, </td>
601 </tr>
602 <tr>
603 <td class="paramkey"></td>
604 <td></td>
605 <td class="paramtype">const <a class="el" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> &amp;&#160;</td>
606 <td class="paramname"><em>tensorInfo</em>, </td>
607 </tr>
608 <tr>
609 <td class="paramkey"></td>
610 <td></td>
611 <td class="paramtype">bool&#160;</td>
612 <td class="paramname"><em>isBackendOptimization</em> = <code>false</code>&#160;</td>
613 </tr>
614 <tr>
615 <td></td>
616 <td>)</td>
617 <td></td><td></td>
618 </tr>
619 </table>
620 </td>
621 <td class="mlabels-right">
622<span class="mlabels"><span class="mlabel">inline</span></span> </td>
623 </tr>
624</table>
625</div><div class="memdoc">
626
627<p class="definition">Definition at line <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00115">115</a> of file <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html">FoldPadIntoLayer2d.hpp</a>.</p>
628<div class="fragment"><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;{</div>
629<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="comment">// Cannot fold Average or L2 pooling if padding exists and the padding method is Exclude.</span></div>
630<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="keywordflow">if</span> (poolDescriptor.m_PoolType != PoolingAlgorithm::Max &amp;&amp;</div>
631<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <a class="code" href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a9f3ad988ab5cf0c11de5380e77bbb50e">IsPooling2dPadded</a>(poolDescriptor) &amp;&amp;</div>
632<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; poolDescriptor.m_PaddingMethod == PaddingMethod::Exclude)</div>
633<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; {</div>
634<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
635<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; }</div>
636<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; </div>
637<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; <span class="comment">// Cannot fold Average pooling if data type is quantized and layout is NHWC in Neon backend.</span></div>
638<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="comment">// Therefore, this specific case will become a backend specific optimization.</span></div>
639<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="keywordflow">if</span> (!isBackendOptimization &amp;&amp;</div>
640<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; tensorInfo.IsQuantized() &amp;&amp;</div>
641<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; poolDescriptor.m_PoolType == PoolingAlgorithm::Average &amp;&amp;</div>
642<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; poolDescriptor.m_DataLayout == DataLayout::NHWC)</div>
643<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; {</div>
644<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
645<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; }</div>
646<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; </div>
647<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; poolDescriptor.m_PaddingMethod = PaddingMethod::IgnoreValue;</div>
648<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; </div>
649<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <span class="keywordflow">return</span> TryFoldPadIntoLayer2d&lt;Pooling2dDescriptor&gt;(padDescriptor, poolDescriptor, tensorInfo);</div>
650<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160;}</div>
651</div><!-- fragment -->
652<p class="reference">References <a class="el" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::Average</a>, <a class="el" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::Exclude</a>, <a class="el" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::IgnoreValue</a>, <a class="el" href="_fold_pad_into_layer2d_8hpp_source.html#l00076">IsPooling2dPadded()</a>, <a class="el" href="_tensor_8cpp_source.html#l00508">TensorInfo::IsQuantized()</a>, <a class="el" href="_descriptors_8hpp_source.html#l00427">Pooling2dDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.html#l00425">Pooling2dDescriptor::m_PaddingMethod</a>, <a class="el" href="_descriptors_8hpp_source.html#l00405">Pooling2dDescriptor::m_PoolType</a>, <a class="el" href="namespacearmnn.html#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::Max</a>, and <a class="el" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p>
653
654</div>
655</div>
656</div><!-- contents -->
657</div><!-- doc-content -->
658<div class="ttc" id="anamespacearmnn_1_1optimizations_1_1pad__fold_html_a9f3ad988ab5cf0c11de5380e77bbb50e"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a9f3ad988ab5cf0c11de5380e77bbb50e">armnn::optimizations::pad_fold::IsPooling2dPadded</a></div><div class="ttdeci">bool IsPooling2dPadded(const Pooling2dDescriptor &amp;poolDescriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.html#l00076">FoldPadIntoLayer2d.hpp:76</a></div></div>
659<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html">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.html#l00017">DataLayoutIndexed.hpp:17</a></div></div>
660<div class="ttc" id="a_assert_8hpp_html_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.html#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.html#l00015">Assert.hpp:15</a></div></div>
661<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html#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.html#l00024">DataLayoutIndexed.hpp:24</a></div></div>
662<div class="ttc" id="anamespacearmnn_1_1optimizations_1_1pad__fold_html_a1112c7c010be092e8d2478e5268666de"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a1112c7c010be092e8d2478e5268666de">armnn::optimizations::pad_fold::GetLowestElement</a></div><div class="ttdeci">float GetLowestElement(const TensorInfo &amp;tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.html#l00026">FoldPadIntoLayer2d.hpp:26</a></div></div>
663<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html#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.html#l00025">DataLayoutIndexed.hpp:25</a></div></div>
664<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html#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.html#l00023">DataLayoutIndexed.hpp:23</a></div></div>
665<div class="ttc" id="anamespacearmnn_1_1optimizations_1_1pad__fold_html_adeaaacf15ed6830d77298930545187e6"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.html#adeaaacf15ed6830d77298930545187e6">armnn::optimizations::pad_fold::TryFoldPadIntoLayer2d</a></div><div class="ttdeci">bool TryFoldPadIntoLayer2d(const PadDescriptor &amp;padDescriptor, Pooling2dDescriptor &amp;poolDescriptor, const TensorInfo &amp;tensorInfo, bool isBackendOptimization=false)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.html#l00115">FoldPadIntoLayer2d.hpp:115</a></div></div>
666<div class="ttc" id="anamespacearmnn_html_a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a"><div class="ttname"><a href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a></div><div class="ttdeci">@ Convolution2d</div></div>
667<div class="ttc" id="anamespacearmnn_1_1optimizations_1_1pad__fold_html_a41605a45fe3f148071b04c7d861f391f"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a41605a45fe3f148071b04c7d861f391f">armnn::optimizations::pad_fold::GetZeroElement</a></div><div class="ttdeci">float GetZeroElement(const TensorInfo &amp;tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.html#l00021">FoldPadIntoLayer2d.hpp:21</a></div></div>
668<div class="ttc" id="anamespacearmnn_1_1optimizations_1_1pad__fold_html_a860dc7bc83a72db266ef5d6759686d24"><div class="ttname"><a href="namespacearmnn_1_1optimizations_1_1pad__fold.html#a860dc7bc83a72db266ef5d6759686d24">armnn::optimizations::pad_fold::IsNeutralElement</a></div><div class="ttdeci">bool IsNeutralElement(const Pooling2dDescriptor &amp;descriptor, const TensorInfo &amp;tensorInfo, const float tensorValue)</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_layer2d_8hpp_source.html#l00068">FoldPadIntoLayer2d.hpp:68</a></div></div>
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