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 <div class="header">
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 <a href="#pub-methods">Public Member Functions</a> &#124;
 <a href="#pub-static-methods">Static Public Member Functions</a>  </div>
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 <div class="title">NEFFTConvolutionLayer Class Reference</div>  </div>
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 <p>Basic function to execute FFT-based convolution on NEON.
  <a href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#details">More...</a></p>

 <p><code>#include &lt;<a class="el" href="_n_e_f_f_t_convolution_layer_8h_source.xhtml">NEFFTConvolutionLayer.h</a>&gt;</code></p>
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 Collaboration diagram for NEFFTConvolutionLayer:</div>
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 <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
 Public Member Functions</h2></td></tr>
 <tr class="memitem:abf5b3544ece1ef65b6388a9967d48575"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#abf5b3544ece1ef65b6388a9967d48575">NEFFTConvolutionLayer</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:abf5b3544ece1ef65b6388a9967d48575"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default constructor.  <a href="#abf5b3544ece1ef65b6388a9967d48575">More...</a><br /></td></tr>
 <tr class="separator:abf5b3544ece1ef65b6388a9967d48575"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a983f530faaccfc0f802e893384e392dc"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#a983f530faaccfc0f802e893384e392dc">NEFFTConvolutionLayer</a> (const <a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;)=delete</td></tr>
 <tr class="memdesc:a983f530faaccfc0f802e893384e392dc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#a983f530faaccfc0f802e893384e392dc">More...</a><br /></td></tr>
 <tr class="separator:a983f530faaccfc0f802e893384e392dc"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a728f7b42859264557782540c639afb6d"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#a728f7b42859264557782540c639afb6d">NEFFTConvolutionLayer</a> (<a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:a728f7b42859264557782540c639afb6d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move constructor.  <a href="#a728f7b42859264557782540c639afb6d">More...</a><br /></td></tr>
 <tr class="separator:a728f7b42859264557782540c639afb6d"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a52842d909fcbcca6b5b50f39e7d8e38d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#a52842d909fcbcca6b5b50f39e7d8e38d">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;)=delete</td></tr>
 <tr class="memdesc:a52842d909fcbcca6b5b50f39e7d8e38d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers)  <a href="#a52842d909fcbcca6b5b50f39e7d8e38d">More...</a><br /></td></tr>
 <tr class="separator:a52842d909fcbcca6b5b50f39e7d8e38d"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:af98ae8acd2ed4fae0b5a7844e89f9543"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#af98ae8acd2ed4fae0b5a7844e89f9543">operator=</a> (<a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&amp;)=default</td></tr>
 <tr class="memdesc:af98ae8acd2ed4fae0b5a7844e89f9543"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move assignment operator.  <a href="#af98ae8acd2ed4fae0b5a7844e89f9543">More...</a><br /></td></tr>
 <tr class="separator:af98ae8acd2ed4fae0b5a7844e89f9543"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a064452b83d4907c001b65886876d89c1"><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_f_f_t_convolution_layer.xhtml#a064452b83d4907c001b65886876d89c1">configure</a> (<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, 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>())</td></tr>
 <tr class="memdesc:a064452b83d4907c001b65886876d89c1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the input and output tensors.  <a href="#a064452b83d4907c001b65886876d89c1">More...</a><br /></td></tr>
 <tr class="separator:a064452b83d4907c001b65886876d89c1"><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_f_f_t_convolution_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_f_f_t_convolution_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>
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 <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:ac89fb11a78baf66222f50cd5ee725ebd"><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_f_f_t_convolution_layer.xhtml#ac89fb11a78baf66222f50cd5ee725ebd">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, 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>())</td></tr>
 <tr class="memdesc:ac89fb11a78baf66222f50cd5ee725ebd"><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_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a>.  <a href="#ac89fb11a78baf66222f50cd5ee725ebd">More...</a><br /></td></tr>
 <tr class="separator:ac89fb11a78baf66222f50cd5ee725ebd"><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 execute FFT-based convolution on NEON. </p>
 <p>This function calls the following NEON functions/kernels:</p>
 <ol type="1">
 <li><a class="el" href="classarm__compute_1_1_n_e_permute.xhtml">NEPermute</a> Permute input if <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51" title="Num samples, height, width, channels.">NHWC(only NCHW is supported)</a>.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_pad_layer.xhtml">NEPadLayer</a> Pad input.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml">NEFFT2D</a> Forward transform to the frequency domain.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_complex_pixel_wise_multiplication.xhtml">NEComplexPixelWiseMultiplication</a> Complex element-wise product of input and the weights.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_reduction_operation.xhtml">NEReductionOperation</a> Reduction across channels.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml">NEFFT2D</a> Inverse transform back to the time domain.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_strided_slice.xhtml">NEStridedSlice</a> Extract valid output.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_arithmetic_addition.xhtml">NEArithmeticAddition</a> Add bias.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_activation_layer.xhtml">NEActivationLayer</a> Perform activation.</li>
 <li><a class="el" href="classarm__compute_1_1_n_e_permute.xhtml">NEPermute</a> Permute output if <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51" title="Num samples, height, width, channels.">NHWC(only NCHW is supported)</a>. </li>
 </ol>

 <p class="definition">Definition at line <a class="el" href="_n_e_f_f_t_convolution_layer_8h_source.xhtml#l00059">59</a> of file <a class="el" href="_n_e_f_f_t_convolution_layer_8h_source.xhtml">NEFFTConvolutionLayer.h</a>.</p>
 </div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
 <a id="abf5b3544ece1ef65b6388a9967d48575"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#abf5b3544ece1ef65b6388a9967d48575">&#9670;&nbsp;</a></span>NEFFTConvolutionLayer() <span class="overload">[1/3]</span></h2>

 <div class="memitem">
 <div class="memproto">
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         <tr>
           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</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_f_f_t_convolution_layer_8cpp_source.xhtml#l00055">55</a> of file <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml">NEFFTConvolutionLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    : _memory_group(memory_manager),</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;      _flip_weights_func(),</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;      _permute_input_func(),</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;      _permute_output_func(),</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;      _permute_weights_func(),</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;      _permute_bias_func(),</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;      _pad_input_func(),</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;      _pad_weights_func(),</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;      _transform_input_func(memory_manager),</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;      _transform_weights_func(),</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;      _itransform_output_func(memory_manager),</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;      _prod_func(),</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;      _reduce_func(),</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;      _extract_output_func(),</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;      _bias_add_func(),</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;      _activation_layer_func(),</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;      _permuted_input(),</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;      _permuted_weights(),</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;      _permuted_bias(),</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;      _permuted_output(),</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;      _padded_input(),</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;      _padded_weights(),</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;      _flip_axis(),</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;      _flipped_weights(),</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      _transformed_input(),</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;      _transformed_weights(),</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;      _input_weights_product(),</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;      _output_product(),</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;      _output_reduced(),</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;      _itransformed_output(),</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;      _reshaped_output(),</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;      _bias_output(),</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;      _original_weights(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;      _original_bias(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;      _is_activationlayer_enabled(<span class="keyword">false</span>),</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;      _needs_permute(<span class="keyword">false</span>),</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;      _has_bias(<span class="keyword">false</span>),</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;      _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;{</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;}</div></div><!-- fragment -->
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 <h2 class="memtitle"><span class="permalink"><a href="#a983f530faaccfc0f802e893384e392dc">&#9670;&nbsp;</a></span>NEFFTConvolutionLayer() <span class="overload">[2/3]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
           <td></td>
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 <span class="mlabels"><span class="mlabel">delete</span></span>  </td>
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 <p>Prevent instances of this class from being copied (As this class contains pointers) </p>

 </div>
 </div>
 <a id="a728f7b42859264557782540c639afb6d"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a728f7b42859264557782540c639afb6d">&#9670;&nbsp;</a></span>NEFFTConvolutionLayer() <span class="overload">[3/3]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> </td>
           <td>(</td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&amp;&#160;</td>
           <td class="paramname"></td><td>)</td>
           <td></td>
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 <p>Default move constructor. </p>

 </div>
 </div>
 <h2 class="groupheader">Member Function Documentation</h2>
 <a id="a064452b83d4907c001b65886876d89c1"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a064452b83d4907c001b65886876d89c1">&#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_tensor.xhtml">ITensor</a> *&#160;</td>
           <td class="paramname"><em>input</em>, </td>
         </tr>
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           <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>
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           <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>
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           <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>
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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>, </td>
         </tr>
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           <td class="paramkey"></td>
           <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>&#160;</td>
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           <td></td>
           <td>)</td>
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 <p>Set the input and output tensors. </p>
 <dl class="section note"><dt>Note</dt><dd>: This function only works with any square kernel size and unit strides for both NCHW and NHWC data layout</dd></dl>
 <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: F32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 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 1D tensor with dimensions [OFM].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>
     <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>
   </table>
   </dd>
 </dl>

 <p class="definition">Definition at line <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml#l00097">97</a> of file <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml">NEFFTConvolutionLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;{</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    _original_bias    = biases;</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;    <span class="comment">// Flat if bias addition is required</span></div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    _has_bias = biases != <span class="keyword">nullptr</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">// Get indices for the width and height</span></div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width  = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <span class="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keyword">const</span> Size2D input_dims  = Size2D(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;tensor_shape()[idx_width], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;tensor_shape()[idx_height]);</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="keyword">const</span> Size2D kernel_size = Size2D(<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#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[idx_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#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[idx_height]);</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="keyword">const</span> Size2D pad_valid   = Size2D(pad_decomposable(input_dims.x() + kernel_size.x() - 1),</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;                                      pad_decomposable(input_dims.y() + kernel_size.y() - 1));</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="comment">// Tensors to use</span></div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    ITensor       *input_to_use   = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="keyword">const</span> ITensor *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    ITensor       *output_to_use  = _has_bias ? &amp;_bias_output : output;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <span class="comment">// Permute bias</span></div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    {</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        _permute_bias_func.<a class="code" href="classarm__compute_1_1_n_e_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(biases, &amp;_permuted_bias, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        _permuted_bias.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#abb9481fe056b9749f9b4c08db101cc15">set_data_layout</a>(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><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;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="comment">// Permute input if needed</span></div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    _needs_permute = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;data_layout() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    {</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_permuted_input);</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        <span class="comment">// Configure the function to transform the input tensor from NHWC -&gt; NCHW</span></div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        _permute_input_func.<a class="code" href="classarm__compute_1_1_n_e_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &amp;_permuted_input, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        _permuted_input.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#abb9481fe056b9749f9b4c08db101cc15">set_data_layout</a>(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</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">// Configure the function to transform the weights tensor from HWI -&gt; IHW</span></div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        _permute_weights_func.<a class="code" href="classarm__compute_1_1_n_e_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &amp;_permuted_weights, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;        _permuted_weights.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#abb9481fe056b9749f9b4c08db101cc15">set_data_layout</a>(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;        input_to_use   = &amp;_permuted_input;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;        weights_to_use = &amp;_permuted_weights;</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    }</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    <span class="comment">// Flip weights</span></div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    _flipped_weights.<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>(weights_to_use-&gt;info()-&gt;clone()-&gt;set_is_resizable(<span class="keyword">true</span>).reset_padding());</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    _flip_axis.<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>(TensorInfo(TensorShape(2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>));</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160; 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   _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_padded_input);</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    _pad_input_func.<a class="code" href="classarm__compute_1_1_n_e_pad_layer.xhtml#ac5a4e9c1564aa9892dfe02cdce2f55b3">configure</a>(input_to_use, &amp;_padded_input, padding_in);</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    {</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;        _permuted_input.<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;    }</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <span class="comment">// Transform input</span></div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_transformed_input);</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    _transform_input_func.<a class="code" href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml#a4d704db199385825fcc6fa9fbed62924">configure</a>(&amp;_padded_input, &amp;_transformed_input, FFT2DInfo());</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    _padded_input.<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="l00170"></a><span class="lineno">  170</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    <span class="comment">// Perform product</span></div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_output_product);</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    _prod_func.<a class="code" href="classarm__compute_1_1_n_e_complex_pixel_wise_multiplication.xhtml#a151f73d8df86064f806f8b3256571b0a">configure</a>(&amp;_transformed_input, &amp;_transformed_weights, &amp;_output_product);</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    _transformed_input.<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="l00175"></a><span class="lineno">  175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160; 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   <span class="comment">// Transform output</span></div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_itransformed_output);</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    FFT2DInfo itranform_info;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    itranform_info.direction = <a class="code" href="namespacearm__compute.xhtml#a86a0c8e195c900a895c249662cfaa564a9f87f02f2da8f99c571b2a1c2a96132b">FFTDirection::Inverse</a>;</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    _itransformed_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>(_output_reduced.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-&gt;<a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()-&gt;set_is_resizable(<span class="keyword">true</span>).set_num_channels(1).reset_padding());</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    _itransform_output_func.<a class="code" href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml#a4d704db199385825fcc6fa9fbed62924">configure</a>(&amp;_output_reduced, &amp;_itransformed_output, itranform_info);</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    _output_reduced.<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="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">// Reshape output</span></div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    TensorShape reshaped_shape = _itransformed_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    reshaped_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(2);</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;    _reshaped_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>(_itransformed_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-&gt;<a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()-&gt;set_tensor_shape(reshaped_shape));</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;    <span class="comment">// Extract correct region</span></div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> start_left = kernel_size.x() - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() - 1;</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> start_top  = kernel_size.y() - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() - 1;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> end_right  = _reshaped_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>() - (kernel_size.x() - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() - 1) - pad_valid.x();</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160; 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   }</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    {</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;        output_to_use = &amp;_permuted_output;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_permuted_output);</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    }</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    _extract_output_func.<a class="code" href="classarm__compute_1_1_n_e_slice.xhtml#a5c2f378cb28b6c33a699fcf3d24b7ab3">configure</a>(&amp;_reshaped_output, output_to_use, Coordinates(start_left, start_top), Coordinates(end_right, end_botton));</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    _reshaped_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="l00210"></a><span class="lineno">  210</span>&#160;    _itransformed_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="l00211"></a><span class="lineno">  211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    <span class="comment">// Add bias</span></div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    {</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        output_to_use = output;</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        {</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;            output_to_use = &amp;_permuted_output;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;            _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&amp;_permuted_output);</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        }</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output_to_use-&gt;info(), *_bias_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>());</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        _bias_add_func.<a class="code" href="classarm__compute_1_1_n_e_arithmetic_addition.xhtml#a17b1a968874510a32f0bdcc78e0cb360">configure</a>(&amp;_bias_output, &amp;_permuted_bias, output_to_use, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fae1c8555fcf0ea2bb648a6fd527d658c0">ConvertPolicy::WRAP</a>);</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        _bias_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="l00224"></a><span class="lineno">  224</span>&#160;    }</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <span class="comment">// Permute output</span></div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160; 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   <span class="keyword">auto</span> axis_data = reinterpret_cast&lt;uint32_t *&gt;(_flip_axis.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>());</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    axis_data[0]   = 0;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    axis_data[1]   = 1;</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_n_e_arithmetic_addition_xhtml_a17b1a968874510a32f0bdcc78e0cb360"><div class="ttname"><a href="classarm__compute_1_1_n_e_arithmetic_addition.xhtml#a17b1a968874510a32f0bdcc78e0cb360">arm_compute::NEArithmeticAddition::configure</a></div><div class="ttdeci">void configure(ITensor *input1, ITensor *input2, ITensor *output, ConvertPolicy policy)</div><div class="ttdoc">Initialise the kernel's inputs, output and conversion policy.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_arithmetic_addition_8cpp_source.xhtml#l00034">NEArithmeticAddition.cpp:34</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_acb74edf42335de0dca0da5158b704c4b"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">arm_compute::TensorShape::remove_dimension</a></div><div class="ttdeci">void remove_dimension(size_t n)</div><div class="ttdoc">Accessor to remove the dimension n from the tensor shape.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00110">TensorShape.h:110</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_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="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">ConvolutionLayer.cpp:183</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ac1a1b012674e0f1de071a611391828ad"><div class="ttname"><a href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">arm_compute::PaddingList</a></div><div class="ttdeci">std::vector&lt; PaddingInfo &gt; PaddingList</div><div class="ttdoc">List of padding information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00455">Types.h:455</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="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00048">Types.h:48</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &amp;info, const TensorShape &amp;shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00202">Helpers.inl:202</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_reverse_xhtml_abaed98d5f33f212b61484a7ef0c53874"><div class="ttname"><a href="classarm__compute_1_1_n_e_reverse.xhtml#abaed98d5f33f212b61484a7ef0c53874">arm_compute::NEReverse::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const ITensor *axis)</div><div class="ttdoc">Initialize the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_reverse_8cpp_source.xhtml#l00031">NEReverse.cpp:31</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_tensor_xhtml_a47d74e4e51f9b1a636c4831bd747a97c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">arm_compute::Tensor::info</a></div><div class="ttdeci">ITensorInfo * 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="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor.cpp:33</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_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_dimensions_xhtml_aa87f8fc26981b0f3228a78c83b95b802"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">arm_compute::Dimensions::x</a></div><div class="ttdeci">T x() const</div><div class="ttdoc">Alias to access the size of the first dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00081">Dimensions.h:81</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a5827eb9cb394e74af87f74bd354fb45ba6970bdc2201030b9c03fbdcf3973858a"><div class="ttname"><a href="namespacearm__compute.xhtml#a5827eb9cb394e74af87f74bd354fb45ba6970bdc2201030b9c03fbdcf3973858a">arm_compute::ReductionOperation::SUM</a></div><div class="ttdoc">Sum.</div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_slice_xhtml_a5c2f378cb28b6c33a699fcf3d24b7ab3"><div class="ttname"><a href="classarm__compute_1_1_n_e_slice.xhtml#a5c2f378cb28b6c33a699fcf3d24b7ab3">arm_compute::NESlice::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const Coordinates &amp;starts, const Coordinates &amp;ends)</div><div class="ttdoc">Configure kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_slice_8cpp_source.xhtml#l00036">NESlice.cpp:36</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_complex_pixel_wise_multiplication_xhtml_a151f73d8df86064f806f8b3256571b0a"><div class="ttname"><a href="classarm__compute_1_1_n_e_complex_pixel_wise_multiplication.xhtml#a151f73d8df86064f806f8b3256571b0a">arm_compute::NEComplexPixelWiseMultiplication::configure</a></div><div class="ttdeci">void configure(ITensor *input1, ITensor *input2, ITensor *output)</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_pixel_wise_multiplication_8cpp_source.xhtml#l00055">NEPixelWiseMultiplication.cpp:55</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::Format::U32</a></div><div class="ttdoc">1 channel, 1 U32 per channel</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel.</div></div>
 <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape &amp; tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div>
 <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_abb9481fe056b9749f9b4c08db101cc15"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#abb9481fe056b9749f9b4c08db101cc15">arm_compute::ITensorInfo::set_data_layout</a></div><div class="ttdeci">virtual ITensorInfo &amp; set_data_layout(const DataLayout &amp;data_layout)=0</div><div class="ttdoc">Set the data layout of the tensor.</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_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr&lt; T &gt; clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a82b8ac759c804bc1fb4e2d21e178fb6fae1c8555fcf0ea2bb648a6fd527d658c0"><div class="ttname"><a href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fae1c8555fcf0ea2bb648a6fd527d658c0">arm_compute::ConvertPolicy::WRAP</a></div><div class="ttdoc">Wrap around.</div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_activation_layer_xhtml_adfb5ef37594fc9371c4a2b95e3d5e31b"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">arm_compute::NEActivationLayer::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, ActivationLayerInfo activation_info)</div><div class="ttdoc">[NEActivationLayer snippet]</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_8cpp_source.xhtml#l00036">NEActivationLayer.cpp:36</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a24954cca5108a24706441fd99a7fb04c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">arm_compute::Tensor::buffer</a></div><div class="ttdeci">uint8_t * buffer() const override</div><div class="ttdoc">Interface to be implemented by the child class to return a pointer to CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00043">Tensor.cpp:43</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_pad_layer_xhtml_ac5a4e9c1564aa9892dfe02cdce2f55b3"><div class="ttname"><a href="classarm__compute_1_1_n_e_pad_layer.xhtml#ac5a4e9c1564aa9892dfe02cdce2f55b3">arm_compute::NEPadLayer::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, const PaddingList &amp;padding, const PixelValue constant_value=PixelValue(), const PaddingMode mode=PaddingMode::CONSTANT)</div><div class="ttdoc">Initialize the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_pad_layer_8cpp_source.xhtml#l00164">NEPadLayer.cpp:164</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a86a0c8e195c900a895c249662cfaa564a9f87f02f2da8f99c571b2a1c2a96132b"><div class="ttname"><a href="namespacearm__compute.xhtml#a86a0c8e195c900a895c249662cfaa564a9f87f02f2da8f99c571b2a1c2a96132b">arm_compute::FFTDirection::Inverse</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_ac4a1050be02b20b3f791b9a483f3abe2"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">arm_compute::Dimensions::y</a></div><div class="ttdeci">T y() const</div><div class="ttdoc">Alias to access the size of the second dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00086">Dimensions.h:86</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_permute_xhtml_a93c836ab36443b23753d99495761daf7"><div class="ttname"><a href="classarm__compute_1_1_n_e_permute.xhtml#a93c836ab36443b23753d99495761daf7">arm_compute::NEPermute::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const PermutationVector &amp;perm)</div><div class="ttdoc">Configure the permute NEON kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_permute_8cpp_source.xhtml#l00031">NEPermute.cpp:31</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape &amp; tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00261">TensorInfo.h:261</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_f_f_t2_d_xhtml_a4d704db199385825fcc6fa9fbed62924"><div class="ttname"><a href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml#a4d704db199385825fcc6fa9fbed62924">arm_compute::NEFFT2D::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const FFT2DInfo &amp;config)</div><div class="ttdoc">Initialise the function's source and destinations.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_f_f_t2_d_8cpp_source.xhtml#l00037">NEFFT2D.cpp:37</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_reduction_operation_xhtml_a61cb078b47a1282258de69e3292dc8c8"><div class="ttname"><a href="classarm__compute_1_1_n_e_reduction_operation.xhtml#a61cb078b47a1282258de69e3292dc8c8">arm_compute::NEReductionOperation::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, unsigned int axis, ReductionOperation op, bool keep_dims=true)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_reduction_operation_8cpp_source.xhtml#l00100">NEReductionOperation.cpp:100</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">arm_compute::test::validation::act_info</a>, <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="_helpers_8inl_source.xhtml#l00202">arm_compute::auto_init_if_empty()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00043">Tensor::buffer()</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable&lt; T &gt;::clone()</a>, <a class="el" href="_n_e_arithmetic_addition_8cpp_source.xhtml#l00034">NEArithmeticAddition::configure()</a>, <a class="el" href="_n_e_reverse_8cpp_source.xhtml#l00031">NEReverse::configure()</a>, <a class="el" href="_n_e_permute_8cpp_source.xhtml#l00031">NEPermute::configure()</a>, <a class="el" href="_n_e_slice_8cpp_source.xhtml#l00036">NESlice::configure()</a>, <a class="el" href="_n_e_f_f_t2_d_8cpp_source.xhtml#l00037">NEFFT2D::configure()</a>, <a class="el" href="_n_e_reduction_operation_8cpp_source.xhtml#l00100">NEReductionOperation::configure()</a>, <a class="el" href="_n_e_pad_layer_8cpp_source.xhtml#l00164">NEPadLayer::configure()</a>, <a class="el" href="_n_e_activation_layer_8cpp_source.xhtml#l00036">NEActivationLayer::configure()</a>, <a class="el" href="_n_e_pixel_wise_multiplication_8cpp_source.xhtml#l00055">NEComplexPixelWiseMultiplication::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="_function_descriptors_8h_source.xhtml#l00048">FFT2DInfo::direction</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor::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="namespacearm__compute.xhtml#a86a0c8e195c900a895c249662cfaa564a9f87f02f2da8f99c571b2a1c2a96132b">arm_compute::Inverse</a>, <a class="el" href="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::NCHW</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::NHWC</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00110">TensorShape::remove_dimension()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#abb9481fe056b9749f9b4c08db101cc15">ITensorInfo::set_data_layout()</a>, <a class="el" href="namespacearm__compute.xhtml#a5827eb9cb394e74af87f74bd354fb45ba6970bdc2201030b9c03fbdcf3973858a">arm_compute::SUM</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00261">TensorInfo::tensor_shape()</a>, <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::U</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::U32</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>, <a class="el" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fae1c8555fcf0ea2bb648a6fd527d658c0">arm_compute::WRAP</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions&lt; T &gt;::x()</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00086">Dimensions&lt; T &gt;::y()</a>.</p>

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 <h2 class="memtitle"><span class="permalink"><a href="#a52842d909fcbcca6b5b50f39e7d8e38d">&#9670;&nbsp;</a></span>operator=() <span class="overload">[1/2]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a>&amp; operator= </td>
           <td>(</td>
           <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&#160;</td>
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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="#af98ae8acd2ed4fae0b5a7844e89f9543">&#9670;&nbsp;</a></span>operator=() <span class="overload">[2/2]</span></h2>

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           <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a>&amp; operator= </td>
           <td>(</td>
           <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a> &amp;&amp;&#160;</td>
           <td class="paramname"></td><td>)</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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           <td class="memname">void prepare </td>
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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_f_f_t_convolution_layer_8cpp_source.xhtml#l00337">337</a> of file <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml">NEFFTConvolutionLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;{</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    {</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        <span class="comment">// Permute bias to NCHW</span></div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;        <span class="keywordflow">if</span>(_original_bias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;        {</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;            _permuted_bias.<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="l00345"></a><span class="lineno">  345</span>&#160;            _permute_bias_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;            _original_bias-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;        }</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        <span class="keyword">const</span> ITensor *cur_weights = _original_weights;</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;        <span class="comment">// Permute weights</span></div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;        <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;        {</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;            <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!cur_weights-&gt;is_used());</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;            _permuted_weights.<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="l00357"></a><span class="lineno">  357</span>&#160;            _permute_weights_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;            cur_weights-&gt;mark_as_unused();</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;            cur_weights = &amp;_permuted_weights;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;        }</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;        <span class="comment">// Flip weights</span></div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;        _flipped_weights.<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="l00364"></a><span class="lineno">  364</span>&#160;        _flip_weights_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;        cur_weights-&gt;mark_as_unused();</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        <span class="comment">// Pad weights</span></div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        _padded_weights.<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="l00369"></a><span class="lineno">  369</span>&#160;        _pad_weights_func.<a class="code" href="classarm__compute_1_1_n_e_pad_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;        _flipped_weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;        _flipped_weights.<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#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;        <span class="comment">// Transform weights to frequency domain</span></div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;        _transformed_weights.<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="l00375"></a><span class="lineno">  375</span>&#160;        _transform_weights_func-&gt;run();</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;        _transform_weights_func.reset();</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;        _padded_weights.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;        _padded_weights.<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#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;    }</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::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_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</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_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::TensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00148">TensorAllocator.cpp:148</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_pad_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_pad_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEPadLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_pad_layer_8cpp_source.xhtml#l00247">NEPadLayer.cpp:247</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="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00148">TensorAllocator::free()</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>, <a class="el" href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder::run()</a>, and <a class="el" href="_n_e_pad_layer_8cpp_source.xhtml#l00247">NEPadLayer::run()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml#l00296">NEFFTConvolutionLayer::run()</a>.</p>

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

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 <div class="memproto">
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   <td class="mlabels-left">
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           <td class="memname">void run </td>
           <td>(</td>
           <td class="paramname"></td><td>)</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_n_e_f_f_t_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" 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="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml#l00296">296</a> of file <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml">NEFFTConvolutionLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;{</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <span class="comment">// Transform input</span></div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    {</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        _permute_input_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    }</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    _pad_input_func.<a class="code" href="classarm__compute_1_1_n_e_pad_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    _transform_input_func.<a class="code" href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    <span class="comment">// Perform operations to frequency domain</span></div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    _prod_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    _reduce_func.<a class="code" href="classarm__compute_1_1_n_e_reduction_operation.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <span class="comment">// Transform output</span></div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    _itransform_output_func.<a class="code" href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    _reshaped_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#a84052cebf66a6126051a166a078253a4">import_memory</a>(_itransformed_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>());</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    _extract_output_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    <span class="comment">// Add bias</span></div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="keywordflow">if</span>(_has_bias)</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    {</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;        _bias_add_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    }</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    {</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;        _permute_output_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    }</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    <span class="comment">// Run activation layer</span></div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    {</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;        _activation_layer_func.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    }</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_n_e_f_f_t_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_f_f_t_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEFFTConvolutionLayer::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_f_f_t_convolution_layer_8cpp_source.xhtml#l00337">NEFFTConvolutionLayer.cpp:337</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::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_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_reduction_operation_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_reduction_operation.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEReductionOperation::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_reduction_operation_8cpp_source.xhtml#l00221">NEReductionOperation.cpp:221</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunction::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_n_e_simple_function_8cpp_source.xhtml#l00036">INESimpleFunction.cpp:36</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_n_e_pad_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_pad_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEPadLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_pad_layer_8cpp_source.xhtml#l00247">NEPadLayer.cpp:247</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_n_e_f_f_t2_d_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_f_f_t2_d.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEFFT2D::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_f_f_t2_d_8cpp_source.xhtml#l00086">NEFFT2D.cpp:86</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a24954cca5108a24706441fd99a7fb04c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">arm_compute::Tensor::buffer</a></div><div class="ttdeci">uint8_t * buffer() const override</div><div class="ttdoc">Interface to be implemented by the child class to return a pointer to CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00043">Tensor.cpp:43</a></div></div>
 <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a84052cebf66a6126051a166a078253a4"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a84052cebf66a6126051a166a078253a4">arm_compute::TensorAllocator::import_memory</a></div><div class="ttdeci">Status import_memory(void *memory)</div><div class="ttdoc">Import an existing memory as a tensor's backing memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00154">TensorAllocator.cpp:154</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00043">Tensor::buffer()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00154">TensorAllocator::import_memory()</a>, <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml#l00337">NEFFTConvolutionLayer::prepare()</a>, <a class="el" href="_i_n_e_simple_function_8cpp_source.xhtml#l00036">INESimpleFunction::run()</a>, <a class="el" href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder::run()</a>, <a class="el" href="_n_e_f_f_t2_d_8cpp_source.xhtml#l00086">NEFFT2D::run()</a>, <a class="el" href="_n_e_reduction_operation_8cpp_source.xhtml#l00221">NEReductionOperation::run()</a>, and <a class="el" href="_n_e_pad_layer_8cpp_source.xhtml#l00247">NEPadLayer::run()</a>.</p>

 </div>
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 <a id="ac89fb11a78baf66222f50cd5ee725ebd"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#ac89fb11a78baf66222f50cd5ee725ebd">&#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>
         </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>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>
         </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>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <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>&#160;</td>
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           <td></td>
           <td>)</td>
           <td></td><td></td>
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 <span class="mlabels"><span class="mlabel">static</span></span>  </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_f_f_t_convolution_layer.xhtml">NEFFTConvolutionLayer</a>. </p>
 <dl class="section note"><dt>Note</dt><dd>: This function only works with any square kernel size and unit strides for both NCHW and NHWC data layout</dd></dl>
 <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: F32. </td></tr>
     <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 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 1D tensor with dimensions [OFM].Data type supported: Same as <code>input</code> </td></tr>
     <tr><td class="paramdir">[in]</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>
     <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>
   </table>
   </dd>
 </dl>
 <dl class="section return"><dt>Returns</dt><dd>a status </dd></dl>

 <p class="definition">Definition at line <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml#l00252">252</a> of file <a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml">NEFFTConvolutionLayer.cpp</a>.</p>
 <div class="fragment"><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;{</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</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>);</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="comment">// Get indices for the width and height</span></div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width  = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <span class="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <span class="keyword">const</span> Size2D kernel_size = Size2D(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;tensor_shape()[idx_width], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;tensor_shape()[idx_height]);</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    <span class="comment">// Strides</span></div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> strides = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride();</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(strides.first != strides.second &amp;&amp; strides.first != 1);</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(kernel_size.x() != kernel_size.y());</div><div class="line"><a name="l00269"></a><span class="lineno">  269</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>.pad_left() != (kernel_size.x() / 2) || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != (kernel_size.x() / 2));</div><div class="line"><a name="l00270"></a><span class="lineno">  270</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>.pad_top() != (kernel_size.y() / 2) || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != (kernel_size.y() / 2));</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    <span class="comment">// Validate biases</span></div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    {</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_channels = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;        <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, biases);</div><div class="line"><a name="l00277"></a><span class="lineno">  277</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#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;tensor_shape()[idx_channels] != biases-&gt;tensor_shape().x());</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    }</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    <span class="comment">// Checks performed when output is configured</span></div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    <span class="keywordflow">if</span>((output != <span class="keyword">nullptr</span>) &amp;&amp; (output-&gt;total_size() != 0))</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;    {</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;        <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output);</div><div class="line"><a name="l00284"></a><span class="lineno">  284</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#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;tensor_shape()[idx_height] != output-&gt;tensor_shape()[idx_height]) || (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;tensor_shape()[idx_width] != output-&gt;tensor_shape()[idx_width]));</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;        <span class="comment">// Validate Activation Layer</span></div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;        <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled())</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;        {</div><div class="line"><a name="l00289"></a><span class="lineno">  289</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_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</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="l00290"></a><span class="lineno">  290</span>&#160;        }</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    }</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;}</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="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">ConvolutionLayer.cpp:183</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</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_n_e_activation_layer_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::NEActivationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &amp;act_info)</div><div class="ttdoc">[NEActivationLayer snippet]</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_8cpp_source.xhtml#l00043">NEActivationLayer.cpp:43</a></div></div>
 <div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00792">Validate.h:792</a></div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</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="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_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</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="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div>
 <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div>
 </div><!-- fragment -->
 <p class="reference">References <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">arm_compute::test::validation::act_info</a>, <a class="el" href="_error_8h_source.xhtml#l00296">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00792">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_validate_8h_source.xhtml#l00545">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</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#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">ITensorInfo::total_size()</a>, <a class="el" href="_n_e_activation_layer_8cpp_source.xhtml#l00043">NEActivationLayer::validate()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions&lt; T &gt;::x()</a>.</p>

 <p class="reference">Referenced by <a class="el" href="_n_e_convolution_layer_8cpp_source.xhtml#l00120">NEConvolutionLayer::get_convolution_method()</a>, and <a class="el" href="_n_e_convolution_layer_8cpp_source.xhtml#l00089">NEConvolutionLayer::validate()</a>.</p>

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 <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_f_f_t_convolution_layer_8h_source.xhtml">NEFFTConvolutionLayer.h</a></li>
 <li>src/runtime/NEON/functions/<a class="el" href="_n_e_f_f_t_convolution_layer_8cpp_source.xhtml">NEFFTConvolutionLayer.cpp</a></li>
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