documentation/classarm__compute_1_1_n_e_locally_connected_layer.xhtml - platform/external/ComputeLibrary - Git at Google

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 <div class="header">
   <div class="summary">
 <a href="#pub-methods">Public Member Functions</a> &#124;
 <a href="#pub-static-methods">Static Public Member Functions</a>  </div>
   <div class="headertitle">
 <div class="title">NELocallyConnectedLayer Class Reference</div>  </div>
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 <p>Basic function to compute the locally connected layer.
  <a href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#details">More...</a></p>

 <p><code>#include &lt;<a class="el" href="_n_e_locally_connected_layer_8h_source.xhtml">NELocallyConnectedLayer.h</a>&gt;</code></p>
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 Collaboration diagram for NELocallyConnectedLayer:</div>
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 Public Member Functions</h2></td></tr>
 <tr class="memitem:a0115186dd4cd64fcca9f32216f5d6639"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#a0115186dd4cd64fcca9f32216f5d6639">NELocallyConnectedLayer</a> (std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt; memory_manager=nullptr)</td></tr>
 <tr class="memdesc:a0115186dd4cd64fcca9f32216f5d6639"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default constructor.  <a href="#a0115186dd4cd64fcca9f32216f5d6639">More...</a><br /></td></tr>
 <tr class="separator:a0115186dd4cd64fcca9f32216f5d6639"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:ad04687d2c3ec4e64e76b4357c25269b4"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#ad04687d2c3ec4e64e76b4357c25269b4">NELocallyConnectedLayer</a> (const <a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;)=delete</td></tr>
 <tr class="memdesc:ad04687d2c3ec4e64e76b4357c25269b4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#ad04687d2c3ec4e64e76b4357c25269b4">More...</a><br /></td></tr>
 <tr class="separator:ad04687d2c3ec4e64e76b4357c25269b4"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a477dfb4eac212309023a3a1ca6284d9c"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#a477dfb4eac212309023a3a1ca6284d9c">NELocallyConnectedLayer</a> (<a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:a477dfb4eac212309023a3a1ca6284d9c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move constructor.  <a href="#a477dfb4eac212309023a3a1ca6284d9c">More...</a><br /></td></tr>
 <tr class="separator:a477dfb4eac212309023a3a1ca6284d9c"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a2011fb7b44e76ebd2957034db21a81b0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#a2011fb7b44e76ebd2957034db21a81b0">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;)=delete</td></tr>
 <tr class="memdesc:a2011fb7b44e76ebd2957034db21a81b0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#a2011fb7b44e76ebd2957034db21a81b0">More...</a><br /></td></tr>
 <tr class="separator:a2011fb7b44e76ebd2957034db21a81b0"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:aae34e0091ee3636e2e9f80d7c69aa3a9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#aae34e0091ee3636e2e9f80d7c69aa3a9">operator=</a> (<a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:aae34e0091ee3636e2e9f80d7c69aa3a9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move assignment operator.  <a href="#aae34e0091ee3636e2e9f80d7c69aa3a9">More...</a><br /></td></tr>
 <tr class="separator:aae34e0091ee3636e2e9f80d7c69aa3a9"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a38198731404a741d75225ae36baf100a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#a38198731404a741d75225ae36baf100a">configure</a> (const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;conv_info)</td></tr>
 <tr class="memdesc:a38198731404a741d75225ae36baf100a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the input and output tensors.  <a href="#a38198731404a741d75225ae36baf100a">More...</a><br /></td></tr>
 <tr class="separator:a38198731404a741d75225ae36baf100a"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr>
 <tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Run the kernels contained in the function.  <a href="#ad1717410afd0be936c6213a63c8005fb">More...</a><br /></td></tr>
 <tr class="separator:ad1717410afd0be936c6213a63c8005fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a> () override</td></tr>
 <tr class="memdesc:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prepare the function for executing.  <a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">More...</a><br /></td></tr>
 <tr class="separator:aa9b93ef660fc3c5b4b19d3fc7b891b77"><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>
 </table><table class="memberdecls">
 <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
 Static Public Member Functions</h2></td></tr>
 <tr class="memitem:a126f91e344585b85ae09a7b76e68c520"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classarm__compute_1_1_status.xhtml">Status</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#a126f91e344585b85ae09a7b76e68c520">validate</a> (const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;conv_info)</td></tr>
 <tr class="memdesc:a126f91e344585b85ae09a7b76e68c520"><td class="mdescLeft">&#160;</td><td class="mdescRight">Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a>.  <a href="#a126f91e344585b85ae09a7b76e68c520">More...</a><br /></td></tr>
 <tr class="separator:a126f91e344585b85ae09a7b76e68c520"><td class="memSeparator" colspan="2">&#160;</td></tr>
 </table>
 <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
 <div class="textblock"><p>Basic function to compute the locally connected layer. </p>
 <p>This function calls the following NEON kernels:</p>
 <ol type="1">
 <li><a class="el" href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml">NEWeightsReshapeKernel</a> (executed only once for each configuration)</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml">NEIm2ColKernel</a></li>
 <li><a class="el" href="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel.xhtml">NELocallyConnectedMatrixMultiplyKernel</a></li>
 <li><a class="el" href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml">NECol2ImKernel</a> </li>
 </ol>

 <p class="definition">Definition at line <a class="el" href="_n_e_locally_connected_layer_8h_source.xhtml#l00051">51</a> of file <a class="el" href="_n_e_locally_connected_layer_8h_source.xhtml">NELocallyConnectedLayer.h</a>.</p>
 </div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
 <a id="a0115186dd4cd64fcca9f32216f5d6639"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a0115186dd4cd64fcca9f32216f5d6639">&#9670;&nbsp;</a></span>NELocallyConnectedLayer() <span class="overload">[1/3]</span></h2>

 <div class="memitem">
 <div class="memproto">
       <table class="memname">
         <tr>
           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> </td>
           <td>(</td>
           <td class="paramtype">std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt;&#160;</td>
           <td class="paramname"><em>memory_manager</em> = <code>nullptr</code></td><td>)</td>
           <td></td>
         </tr>
       </table>
 </div><div class="memdoc">

 <p>Default constructor. </p>

 <p class="definition">Definition at line <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00074">74</a> of file <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml">NELocallyConnectedLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(),</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;      _is_prepared(<span class="keyword">false</span>), _original_weights(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;{</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;}</div></div><!-- fragment -->
 </div>
 </div>
 <a id="ad04687d2c3ec4e64e76b4357c25269b4"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#ad04687d2c3ec4e64e76b4357c25269b4">&#9670;&nbsp;</a></span>NELocallyConnectedLayer() <span class="overload">[2/3]</span></h2>

 <div class="memitem">
 <div class="memproto">
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   <tr>
   <td class="mlabels-left">
       <table class="memname">
         <tr>
           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
           <td></td>
         </tr>
       </table>
   </td>
   <td class="mlabels-right">
 <span class="mlabels"><span class="mlabel">delete</span></span>  </td>
   </tr>
 </table>
 </div><div class="memdoc">

 <p>Prevent instances of this class from being copied (As this class contains pointers) </p>

 </div>
 </div>
 <a id="a477dfb4eac212309023a3a1ca6284d9c"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a477dfb4eac212309023a3a1ca6284d9c">&#9670;&nbsp;</a></span>NELocallyConnectedLayer() <span class="overload">[3/3]</span></h2>

 <div class="memitem">
 <div class="memproto">
 <table class="mlabels">
   <tr>
   <td class="mlabels-left">
       <table class="memname">
         <tr>
           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> </td>
           <td>(</td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;&amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
           <td></td>
         </tr>
       </table>
   </td>
   <td class="mlabels-right">
 <span class="mlabels"><span class="mlabel">default</span></span>  </td>
   </tr>
 </table>
 </div><div class="memdoc">

 <p>Default move constructor. </p>

 </div>
 </div>
 <h2 class="groupheader">Member Function Documentation</h2>
 <a id="a38198731404a741d75225ae36baf100a"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a38198731404a741d75225ae36baf100a">&#9670;&nbsp;</a></span>configure()</h2>

 <div class="memitem">
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           <td class="memname">void configure </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>input</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</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_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>biases</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>output</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <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>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
         </tr>
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 </div><div class="memdoc">

 <p>Set the input and output tensors. </p>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: F16, F32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor. Weights are 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches]. Data type supported:Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Biases tensor. Shared biases supported. Biases are 2D tensor with dimensions [OFM, num_patches]. Data type supported:Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Contains padding and stride information described in <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>. </td></tr>
   </table>
   </dd>
 </dl>

 <p class="definition">Definition at line <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00124">124</a> of file <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml">NELocallyConnectedLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;{</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#a126f91e344585b85ae09a7b76e68c520">NELocallyConnectedLayer::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), biases == <span class="keyword">nullptr</span> ? nullptr : biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>));</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="keywordtype">bool</span> _has_bias    = (biases != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    _is_prepared      = <span class="keyword">false</span>;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_width  = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(0);</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1);</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;dimension(0), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;dimension(1), kernel_width, kernel_height,</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;                                                 <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="comment">// Calculate intermediate buffer shapes</span></div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_wr;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_gemm;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    calculate_shapes(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), biases == <span class="keyword">nullptr</span> ? nullptr : biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, shape_wr, shape_im2col, shape_gemm);</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    _weights_reshaped.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape_wr, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>()));</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    _input_im2col_reshaped.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape_im2col, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;data_type()));</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    _gemm_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape_gemm, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;data_type()));</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    <span class="comment">// Manage intermediate buffers</span></div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_input_im2col_reshaped);</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_gemm_output);</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    <span class="comment">// Configure kernels</span></div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    _input_im2col_kernel.<a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a585edc13576fe5f51f7cc493751fef52">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &amp;_input_im2col_reshaped, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, _has_bias);</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    _weights_reshape_kernel.<a class="code" href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml#a82ef5733f0c6bf93473ec5f12c067338">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, &amp;_weights_reshaped);</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    _mm_kernel.<a class="code" href="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel.xhtml#a00d5b673b5a22ab77fde751ff5330411">configure</a>(&amp;_input_im2col_reshaped, &amp;_weights_reshaped, &amp;_gemm_output);</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    _output_col2im_kernel.<a class="code" href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a5617685de2460c02aa189c0134880c9e">configure</a>(&amp;_gemm_output, output, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(conv_w, conv_h));</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    <span class="comment">// Allocate intermediate tensors</span></div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    _input_im2col_reshaped.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    _gemm_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_n_e_weights_reshape_kernel_xhtml_a82ef5733f0c6bf93473ec5f12c067338"><div class="ttname"><a href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml#a82ef5733f0c6bf93473ec5f12c067338">arm_compute::NEWeightsReshapeKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *bias, ITensor *output)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_weights_reshape_kernel_8cpp_source.xhtml#l00097">NEWeightsReshapeKernel.cpp:97</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor.cpp:41</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &amp;allocator, const Coordinates &amp;coords, TensorInfo &amp;sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</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#l00597">Winograd.cpp:597</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_col2_im_kernel_xhtml_a5617685de2460c02aa189c0134880c9e"><div class="ttname"><a href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a5617685de2460c02aa189c0134880c9e">arm_compute::NECol2ImKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const Size2D &amp;convolved_dims)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00107">NECol2ImKernel.cpp:107</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a138beaeb1260b90cb03bc3f761628724"><div class="ttname"><a href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00402">Utils.cpp:402</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::TensorInfo::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type used for each element of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00265">TensorInfo.h:265</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_locally_connected_layer_xhtml_a126f91e344585b85ae09a7b76e68c520"><div class="ttname"><a href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#a126f91e344585b85ae09a7b76e68c520">arm_compute::NELocallyConnectedLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &amp;conv_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NELocallyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00080">NELocallyConnectedLayer.cpp:80</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel_xhtml_a00d5b673b5a22ab77fde751ff5330411"><div class="ttname"><a href="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel.xhtml#a00d5b673b5a22ab77fde751ff5330411">arm_compute::NELocallyConnectedMatrixMultiplyKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input0, const ITensor *input1, ITensor *output)</div><div class="ttdoc">Initialise the kernel's input and output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_locally_connected_matrix_multiply_kernel_8cpp_source.xhtml#l00340">NELocallyConnectedMatrixMultiplyKernel.cpp:340</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</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="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_im2_col_kernel_xhtml_a585edc13576fe5f51f7cc493751fef52"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a585edc13576fe5f51f7cc493751fef52">arm_compute::NEIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const Size2D &amp;kernel_dims, const PadStrideInfo &amp;conv_info, bool has_bias, const Size2D &amp;dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00350">NEIm2ColKernel.cpp:350</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00455">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_n_e_locally_connected_matrix_multiply_kernel_8cpp_source.xhtml#l00340">NELocallyConnectedMatrixMultiplyKernel::configure()</a>, <a class="el" href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00107">NECol2ImKernel::configure()</a>, <a class="el" href="_n_e_weights_reshape_kernel_8cpp_source.xhtml#l00097">NEWeightsReshapeKernel::configure()</a>, <a class="el" href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00350">NEIm2ColKernel::configure()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00265">TensorInfo::data_type()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00232">TensorInfo::dimension()</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor::info()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator::init()</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00402">arm_compute::scaled_dimensions()</a>, <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00080">NELocallyConnectedLayer::validate()</a>, and <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_o_n_2_locally_connected_8cpp_source.xhtml#l00112">arm_compute::test::validation::DATA_TEST_CASE()</a>.</p>

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 <h2 class="memtitle"><span class="permalink"><a href="#a2011fb7b44e76ebd2957034db21a81b0">&#9670;&nbsp;</a></span>operator=() <span class="overload">[1/2]</span></h2>

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 <p>Prevent instances of this class from being copied (As this class contains pointers) </p>

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 <h2 class="memtitle"><span class="permalink"><a href="#aae34e0091ee3636e2e9f80d7c69aa3a9">&#9670;&nbsp;</a></span>operator=() <span class="overload">[2/2]</span></h2>

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           <td>(</td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a> &amp;&amp;&#160;</td>
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 <p>Default move assignment operator. </p>

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 <h2 class="memtitle"><span class="permalink"><a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">&#9670;&nbsp;</a></span>prepare()</h2>

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 <p>Prepare the function for executing. </p>
 <p>Any one off pre-processing step required by the function is handled here</p>
 <dl class="section note"><dt>Note</dt><dd>Prepare stage might not need all the function's buffers' backing memory to be available in order to execute </dd></dl>

 <p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p>

 <p class="definition">Definition at line <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00183">183</a> of file <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml">NELocallyConnectedLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;{</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    {</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        <span class="comment">// Run weights reshaping and mark original weights tensor as unused</span></div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        _weights_reshaped.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_weights_reshape_kernel, 3);</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        _original_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    }</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</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#l00466">Error.h:466</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &amp;hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div>
 <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div>
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 <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00162">ITensor::is_used()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00167">NELocallyConnectedLayer::run()</a>.</p>

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 <h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">&#9670;&nbsp;</a></span>run()</h2>

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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>
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 <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>
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 <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="_n_e_locally_connected_layer_8cpp_source.xhtml#l00167">167</a> of file <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml">NELocallyConnectedLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;{</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <span class="comment">// Run input reshaping</span></div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_input_im2col_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    <span class="comment">// Runs GEMM on reshaped matrices</span></div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_mm_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <span class="comment">// Reshape output matrix</span></div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_output_col2im_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_locally_connected_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NELocallyConnectedLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00183">NELocallyConnectedLayer.cpp:183</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00082">IMemoryGroup.h:82</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &amp;hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div>
 <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00183">NELocallyConnectedLayer::prepare()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p>

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 <h2 class="memtitle"><span class="permalink"><a href="#a126f91e344585b85ae09a7b76e68c520">&#9670;&nbsp;</a></span>validate()</h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> validate </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>input</em>, </td>
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         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</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_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>biases</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
           <td class="paramname"><em>output</em>, </td>
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           <td class="paramkey"></td>
           <td></td>
           <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>&#160;</td>
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           <td>)</td>
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 <p>Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_locally_connected_layer.xhtml">NELocallyConnectedLayer</a>. </p>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Input tensor info. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: F16, F32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor info. Weights are 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches]. Data type supported:Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Biases tensor info. Shared biases supported. Biases are 2D tensor with dimensions [OFM, num_patches]. Data type supported:Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">output</td><td>Output tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as <code>input</code>. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Contains padding and stride information described in <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>.</td></tr>
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 <dl class="section return"><dt>Returns</dt><dd>a status </dd></dl>

 <p class="definition">Definition at line <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00080">80</a> of file <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml">NELocallyConnectedLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;{</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(2) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;dimension(2));</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.padding_is_symmetric());</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a> = (biases != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>)</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    {</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(3));</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() &gt; 2);</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    }</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_width  = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(0);</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(1);</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;dimension(0), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;dimension(1), kernel_width, kernel_height,</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;                                                 <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>((output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != conv_w) || (output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) != conv_h), <span class="stringliteral">&quot;Output shape does not match the expected one&quot;</span>);</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(4) != (conv_w * conv_h), <span class="stringliteral">&quot;Weights shape does not match the expected one&quot;</span>);</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="comment">// Calculate intermediate buffer shapes</span></div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_wr;</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_gemm;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    calculate_shapes(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <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>, shape_wr, shape_im2col, shape_gemm);</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> weights_reshaped_info(shape_wr, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;data_type());</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_im2col_reshaped_info(shape_im2col, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;data_type());</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> gemm_output_info(shape_gemm, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;data_type());</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">NEIm2ColKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &amp;input_im2col_reshaped_info, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>));</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml#aaa0ba7f013b026d5f823d3193371be59">NEWeightsReshapeKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, &amp;weights_reshaped_info));</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel.xhtml#a0d647d83c8512fa95fa9adb8fb3e0cab">NELocallyConnectedMatrixMultiplyKernel::validate</a>(&amp;input_im2col_reshaped_info, &amp;weights_reshaped_info, &amp;gemm_output_info));</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a35842b155e2204bb6831588a0ffcc6d3">NECol2ImKernel::validate</a>(&amp;gemm_output_info, output, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(conv_w, conv_h)));</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel_xhtml_a0d647d83c8512fa95fa9adb8fb3e0cab"><div class="ttname"><a href="classarm__compute_1_1_n_e_locally_connected_matrix_multiply_kernel.xhtml#a0d647d83c8512fa95fa9adb8fb3e0cab">arm_compute::NELocallyConnectedMatrixMultiplyKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NELocallyConnectedMatrix...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_locally_connected_matrix_multiply_kernel_8cpp_source.xhtml#l00357">NELocallyConnectedMatrixMultiplyKernel.cpp:357</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#l00597">Winograd.cpp:597</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00204">Error.h:204</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00296">Error.h:296</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_weights_reshape_kernel_xhtml_aaa0ba7f013b026d5f823d3193371be59"><div class="ttname"><a href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml#aaa0ba7f013b026d5f823d3193371be59">arm_compute::NEWeightsReshapeKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEWeightsReshapeKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_weights_reshape_kernel_8cpp_source.xhtml#l00119">NEWeightsReshapeKernel.cpp:119</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a138beaeb1260b90cb03bc3f761628724"><div class="ttname"><a href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00402">Utils.cpp:402</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div>
 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a9aeced5a5128f60a31ea3e327a45ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">arm_compute::test::validation::has_bias</a></div><div class="ttdeci">const bool has_bias</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00147">Im2Col.cpp:147</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_col2_im_kernel_xhtml_a35842b155e2204bb6831588a0ffcc6d3"><div class="ttname"><a href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a35842b155e2204bb6831588a0ffcc6d3">arm_compute::NECol2ImKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &amp;convolved_dims)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NECol2ImKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00138">NECol2ImKernel.cpp:138</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</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="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div>
 <div class="ttc" id="_error_8h_xhtml_a1c69762a42ab8add645d0a949b6f4b1f"><div class="ttname"><a href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_im2_col_kernel_xhtml_a4e256965ba7798ffe1358469be661e5a"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">arm_compute::NEIm2ColKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &amp;kernel_dims, const PadStrideInfo &amp;conv_info, bool has_bias, const Size2D &amp;dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEIm2ColKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00423">NEIm2ColKernel.cpp:423</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="_error_8h_source.xhtml#l00296">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_error_8h_source.xhtml#l00244">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>, <a class="el" href="_validate_8h_source.xhtml#l00163">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00147">arm_compute::test::validation::has_bias</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00402">arm_compute::scaled_dimensions()</a>, <a class="el" href="_n_e_locally_connected_matrix_multiply_kernel_8cpp_source.xhtml#l00357">NELocallyConnectedMatrixMultiplyKernel::validate()</a>, <a class="el" href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00138">NECol2ImKernel::validate()</a>, <a class="el" href="_n_e_weights_reshape_kernel_8cpp_source.xhtml#l00119">NEWeightsReshapeKernel::validate()</a>, <a class="el" href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00423">NEIm2ColKernel::validate()</a>, and <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml#l00124">NELocallyConnectedLayer::configure()</a>.</p>

 </div>
 </div>
 <hr/>The documentation for this class was generated from the following files:<ul>
 <li>arm_compute/runtime/NEON/functions/<a class="el" href="_n_e_locally_connected_layer_8h_source.xhtml">NELocallyConnectedLayer.h</a></li>
 <li>src/runtime/NEON/functions/<a class="el" href="_n_e_locally_connected_layer_8cpp_source.xhtml">NELocallyConnectedLayer.cpp</a></li>
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