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<a href="#pub-methods">Public Member Functions</a> </div>
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<div class="title">GCDepthwiseConvolutionLayer3x3 Class Reference</div> </div>
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<p>Basic function to execute a depthwise convolution for kernel size 3x3xC.
<a href="classarm__compute_1_1_g_c_depthwise_convolution_layer3x3.xhtml#details">More...</a></p>
<p><code>#include &lt;<a class="el" href="_g_c_depthwise_convolution_layer_8h_source.xhtml">GCDepthwiseConvolutionLayer.h</a>&gt;</code></p>
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Collaboration diagram for GCDepthwiseConvolutionLayer3x3:</div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a4bda9e07bd18e83b4ef18d3c7abb0632"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_depthwise_convolution_layer3x3.xhtml#a4bda9e07bd18e83b4ef18d3c7abb0632">GCDepthwiseConvolutionLayer3x3</a> ()</td></tr>
<tr class="memdesc:a4bda9e07bd18e83b4ef18d3c7abb0632"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default constructor. <a href="#a4bda9e07bd18e83b4ef18d3c7abb0632">More...</a><br /></td></tr>
<tr class="separator:a4bda9e07bd18e83b4ef18d3c7abb0632"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a763943648ceac09f01689e6eb4bccc26"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_depthwise_convolution_layer3x3.xhtml#a763943648ceac09f01689e6eb4bccc26">configure</a> (<a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;conv_info, unsigned int depth_multiplier=1, const <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &amp;act_info=<a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(), const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &amp;dilation=<a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U))</td></tr>
<tr class="memdesc:a763943648ceac09f01689e6eb4bccc26"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initialize the function's source, destination, conv and border_size. <a href="#a763943648ceac09f01689e6eb4bccc26">More...</a><br /></td></tr>
<tr class="separator:a763943648ceac09f01689e6eb4bccc26"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a92fe532c342ae2b07956a65520c05362"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_depthwise_convolution_layer3x3.xhtml#a92fe532c342ae2b07956a65520c05362">run</a> () override final</td></tr>
<tr class="memdesc:a92fe532c342ae2b07956a65520c05362"><td class="mdescLeft">&#160;</td><td class="mdescRight">Run the kernels contained in the function. <a href="#a92fe532c342ae2b07956a65520c05362">More...</a><br /></td></tr>
<tr class="separator:a92fe532c342ae2b07956a65520c05362"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classarm__compute_1_1_i_function"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarm__compute_1_1_i_function')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarm__compute_1_1_i_function.xhtml">IFunction</a></td></tr>
<tr class="memitem:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">~IFunction</a> ()=default</td></tr>
<tr class="memdesc:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <a href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">More...</a><br /></td></tr>
<tr class="separator:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a820f7291c24155a2980512fae45aac26 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">prepare</a> ()</td></tr>
<tr class="memdesc:a820f7291c24155a2980512fae45aac26 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prepare the function for executing. <a href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">More...</a><br /></td></tr>
<tr class="separator:a820f7291c24155a2980512fae45aac26 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Basic function to execute a depthwise convolution for kernel size 3x3xC. </p>
<p>This function calls the following OpenGLES kernels:</p>
<ol type="1">
<li><a class="el" href="classarm__compute_1_1_g_c_depthwise_convolution_layer3x3_kernel.xhtml">GCDepthwiseConvolutionLayer3x3Kernel</a></li>
<li><a class="el" href="classarm__compute_1_1_g_c_fill_border_kernel.xhtml">GCFillBorderKernel</a> (if pad_x or pad_y &gt; 0) </li>
</ol>
<p class="definition">Definition at line <a class="el" href="_g_c_depthwise_convolution_layer_8h_source.xhtml#l00044">44</a> of file <a class="el" href="_g_c_depthwise_convolution_layer_8h_source.xhtml">GCDepthwiseConvolutionLayer.h</a>.</p>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="a4bda9e07bd18e83b4ef18d3c7abb0632"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a4bda9e07bd18e83b4ef18d3c7abb0632">&#9670;&nbsp;</a></span>GCDepthwiseConvolutionLayer3x3()</h2>
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<td class="memname"><a class="el" href="classarm__compute_1_1_g_c_depthwise_convolution_layer3x3.xhtml">GCDepthwiseConvolutionLayer3x3</a> </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
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<p>Default constructor. </p>
<p class="definition">Definition at line <a class="el" href="_g_c_depthwise_convolution_layer_8cpp_source.xhtml#l00033">33</a> of file <a class="el" href="_g_c_depthwise_convolution_layer_8cpp_source.xhtml">GCDepthwiseConvolutionLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; : _kernel(<span class="keyword">nullptr</span>), _border_handler(), _shift_handler(), _activationlayer_function(), _is_activationlayer_enabled(<span class="keyword">false</span>)</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;}</div></div><!-- fragment -->
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<h2 class="groupheader">Member Function Documentation</h2>
<a id="a763943648ceac09f01689e6eb4bccc26"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a763943648ceac09f01689e6eb4bccc26">&#9670;&nbsp;</a></span>configure()</h2>
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<td class="memname">void configure </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *&#160;</td>
<td class="paramname"><em>input</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *&#160;</td>
<td class="paramname"><em>weights</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *&#160;</td>
<td class="paramname"><em>biases</em>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *&#160;</td>
<td class="paramname"><em>output</em>, </td>
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<td class="paramtype">const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;&#160;</td>
<td class="paramname"><em>conv_info</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">unsigned int&#160;</td>
<td class="paramname"><em>depth_multiplier</em> = <code>1</code>, </td>
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<td></td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &amp;&#160;</td>
<td class="paramname"><em>act_info</em> = <code><a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>()</code>, </td>
</tr>
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<td class="paramkey"></td>
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<td class="paramtype">const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &amp;&#160;</td>
<td class="paramname"><em>dilation</em> = <code><a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U,&#160;1U)</code>&#160;</td>
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<td></td>
<td>)</td>
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<p>Initialize the function's source, destination, conv and border_size. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in,out]</td><td class="paramname">input</td><td>Source tensor. Data type supported: F16. (Written to only for border filling). </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor. A 3D tensor with shape [3, 3, IFM]. Data type supported: Same as <code>input</code>. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. Data type supported: Same as <code>input</code>. </td></tr>
<tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Destination tensor. Data type supported: same as <code>input</code>. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Padding and stride information to use for the convolution. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">depth_multiplier</td><td>(Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">act_info</td><td>(Optional) Activation layer information in case of a fused activation. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">dilation</td><td>(Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only. </td></tr>
</table>
</dd>
</dl>
<p class="definition">Definition at line <a class="el" href="_g_c_depthwise_convolution_layer_8cpp_source.xhtml#l00038">38</a> of file <a class="el" href="_g_c_depthwise_convolution_layer_8cpp_source.xhtml">GCDepthwiseConvolutionLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;{</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x() != 1 || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.y() != 1);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="keyword">auto</span> k = arm_compute::support::cpp14::make_unique&lt;GCDepthwiseConvolutionLayer3x3Kernel&gt;();</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; k-&gt;configure(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; _kernel = std::move(k);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <span class="comment">// Configure border handler</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; _border_handler.<a class="code" href="classarm__compute_1_1_g_c_fill_border_kernel.xhtml#a148acc5bac0dddc8d512b4d91bd2a7ba">configure</a>(input, _kernel-&gt;border_size(), <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>, <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a>());</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; _shift_handler.<a class="code" href="classarm__compute_1_1_g_c_tensor_shift_kernel.xhtml#a2a2ddfadb250e8a4c4d5db67f048f2e8">configure</a>(input);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <span class="comment">//Configure Activation Layer</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; _is_activationlayer_enabled = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled();</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; {</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; _activationlayer_function.<a class="code" href="classarm__compute_1_1_g_c_activation_layer.xhtml#a0fdcd48f36eb1310d56f0f0d5ce9ab00">configure</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml">arm_compute::PixelValue</a></div><div class="ttdoc">Class describing the value of a pixel for any image format.</div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00034">PixelValue.h:34</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">ConvolutionLayer.cpp:175</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00599">Winograd.cpp:599</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_fill_border_kernel_xhtml_a148acc5bac0dddc8d512b4d91bd2a7ba"><div class="ttname"><a href="classarm__compute_1_1_g_c_fill_border_kernel.xhtml#a148acc5bac0dddc8d512b4d91bd2a7ba">arm_compute::GCFillBorderKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *tensor, BorderSize border_size, BorderMode border_mode, const PixelValue &amp;constant_border_value=PixelValue())</div><div class="ttdoc">Initialise the kernel's input, output and border mode.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_fill_border_kernel_8cpp_source.xhtml#l00060">GCFillBorderKernel.cpp:60</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_activation_layer_xhtml_a0fdcd48f36eb1310d56f0f0d5ce9ab00"><div class="ttname"><a href="classarm__compute_1_1_g_c_activation_layer.xhtml#a0fdcd48f36eb1310d56f0f0d5ce9ab00">arm_compute::GCActivationLayer::configure</a></div><div class="ttdeci">void configure(IGCTensor *input, IGCTensor *output, ActivationLayerInfo act_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_activation_layer_8cpp_source.xhtml#l00032">GCActivationLayer.cpp:32</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">arm_compute::PaddingMode::CONSTANT</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_tensor_shift_kernel_xhtml_a2a2ddfadb250e8a4c4d5db67f048f2e8"><div class="ttname"><a href="classarm__compute_1_1_g_c_tensor_shift_kernel.xhtml#a2a2ddfadb250e8a4c4d5db67f048f2e8">arm_compute::GCTensorShiftKernel::configure</a></div><div class="ttdeci">void configure(IGCTensor *input)</div><div class="ttdoc">Set the input of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_tensor_shift_kernel_8cpp_source.xhtml#l00045">GCTensorShiftKernel.cpp:45</a></div></div>
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<p class="reference">References <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">arm_compute::test::validation::act_info</a>, <a class="el" href="_error_8h_source.xhtml#l00337">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_error_8h_source.xhtml#l00160">ARM_COMPUTE_UNUSED</a>, <a class="el" href="_g_c_activation_layer_8cpp_source.xhtml#l00032">GCActivationLayer::configure()</a>, <a class="el" href="_g_c_fill_border_kernel_8cpp_source.xhtml#l00060">GCFillBorderKernel::configure()</a>, <a class="el" href="_g_c_tensor_shift_kernel_8cpp_source.xhtml#l00045">GCTensorShiftKernel::configure()</a>, <a class="el" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">arm_compute::CONSTANT</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00599">arm_compute::test::validation::conv_info</a>, <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">arm_compute::test::validation::dilation</a>, and <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">arm_compute::test::validation::weights</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a92fe532c342ae2b07956a65520c05362">&#9670;&nbsp;</a></span>run()</h2>
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<td class="memname">void run </td>
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<td class="paramname"></td><td>)</td>
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<p>Run the kernels contained in the function. </p>
<p>For NEON kernels:</p><ul>
<li>Multi-threading is used for the kernels which are parallelisable.</li>
<li>By default std::thread::hardware_concurrency() threads are used.</li>
</ul>
<dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl>
<p>For OpenCL kernels:</p><ul>
<li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li>
<li>The queue is then flushed.</li>
</ul>
<dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd>
<dd>
Will call <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl>
<p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p>
<p class="definition">Definition at line <a class="el" href="_g_c_depthwise_convolution_layer_8cpp_source.xhtml#l00061">61</a> of file <a class="el" href="_g_c_depthwise_convolution_layer_8cpp_source.xhtml">GCDepthwiseConvolutionLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;{</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_shift_handler, <span class="keyword">false</span>);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_border_handler, <span class="keyword">false</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(*_kernel);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="comment">// Run Activation Layer</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; {</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; _activationlayer_function.<a class="code" href="classarm__compute_1_1_i_g_c_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; }</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a66a29e27a51a13250143981b0ee4ad19"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">arm_compute::GCScheduler::dispatch</a></div><div class="ttdeci">void dispatch(IGCKernel &amp;kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00069">GCScheduler.cpp:69</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_g_c_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_g_c_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::IGCSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_g_c_simple_function_8cpp_source.xhtml#l00037">IGCSimpleFunction.cpp:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a2dcf87458fcfdfb5e9fdd369e0320d78"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">arm_compute::GCScheduler::memory_barrier</a></div><div class="ttdeci">void memory_barrier()</div><div class="ttdoc">Defines a barrier ordering memory transactions.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00078">GCScheduler.cpp:78</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a9c5f715748222ab9607cc52134b36b0b"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">arm_compute::GCScheduler::get</a></div><div class="ttdeci">static GCScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00062">GCScheduler.cpp:62</a></div></div>
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<p class="reference">References <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00069">GCScheduler::dispatch()</a>, <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00062">GCScheduler::get()</a>, <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00078">GCScheduler::memory_barrier()</a>, and <a class="el" href="_i_g_c_simple_function_8cpp_source.xhtml#l00037">IGCSimpleFunction::run()</a>.</p>
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<hr/>The documentation for this class was generated from the following files:<ul>
<li>arm_compute/runtime/GLES_COMPUTE/functions/<a class="el" href="_g_c_depthwise_convolution_layer_8h_source.xhtml">GCDepthwiseConvolutionLayer.h</a></li>
<li>src/runtime/GLES_COMPUTE/functions/<a class="el" href="_g_c_depthwise_convolution_layer_8cpp_source.xhtml">GCDepthwiseConvolutionLayer.cpp</a></li>
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