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| <div class="title">CLGEMMConvolutionLayer Class Reference</div> </div> |
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| |
| <p>Basic function to compute the convolution layer. |
| <a href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_c_l_g_e_m_m_convolution_layer_8h_source.xhtml">CLGEMMConvolutionLayer.h</a>></code></p> |
| <div class="dynheader"> |
| Collaboration diagram for CLGEMMConvolutionLayer:</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:ab8508bf65741613fe624bdeaadd7a7f8"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#ab8508bf65741613fe624bdeaadd7a7f8">CLGEMMConvolutionLayer</a> (std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > memory_manager=nullptr, <a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> *weights_manager=nullptr)</td></tr> |
| <tr class="memdesc:ab8508bf65741613fe624bdeaadd7a7f8"><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#ab8508bf65741613fe624bdeaadd7a7f8">More...</a><br /></td></tr> |
| <tr class="separator:ab8508bf65741613fe624bdeaadd7a7f8"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a4229132541843309482de80bc7083fff"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#a4229132541843309482de80bc7083fff">CLGEMMConvolutionLayer</a> (const <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a4229132541843309482de80bc7083fff"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a4229132541843309482de80bc7083fff">More...</a><br /></td></tr> |
| <tr class="separator:a4229132541843309482de80bc7083fff"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:aec4977cc997d5b9a644050a0059485ed"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#aec4977cc997d5b9a644050a0059485ed">CLGEMMConvolutionLayer</a> (<a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:aec4977cc997d5b9a644050a0059485ed"><td class="mdescLeft"> </td><td class="mdescRight">Default move constructor. <a href="#aec4977cc997d5b9a644050a0059485ed">More...</a><br /></td></tr> |
| <tr class="separator:aec4977cc997d5b9a644050a0059485ed"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a025e6bce0640a6cb5ecdab8a3e57f9a0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#a025e6bce0640a6cb5ecdab8a3e57f9a0">operator=</a> (const <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a025e6bce0640a6cb5ecdab8a3e57f9a0"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a025e6bce0640a6cb5ecdab8a3e57f9a0">More...</a><br /></td></tr> |
| <tr class="separator:a025e6bce0640a6cb5ecdab8a3e57f9a0"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a78f1fff174957ab8dd876ee696d5a749"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#a78f1fff174957ab8dd876ee696d5a749">operator=</a> (<a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a78f1fff174957ab8dd876ee696d5a749"><td class="mdescLeft"> </td><td class="mdescRight">Default move assignment operator. <a href="#a78f1fff174957ab8dd876ee696d5a749">More...</a><br /></td></tr> |
| <tr class="separator:a78f1fff174957ab8dd876ee696d5a749"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ac485da01d91da9e1c97a7da2b9b91ad5"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#ac485da01d91da9e1c97a7da2b9b91ad5">configure</a> (const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &conv_info, const <a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a> &weights_info=<a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(), const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &dilation=<a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)</td></tr> |
| <tr class="memdesc:ac485da01d91da9e1c97a7da2b9b91ad5"><td class="mdescLeft"> </td><td class="mdescRight">Set the input and output tensors. <a href="#ac485da01d91da9e1c97a7da2b9b91ad5">More...</a><br /></td></tr> |
| <tr class="separator:ac485da01d91da9e1c97a7da2b9b91ad5"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr> |
| <tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft"> </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"> </td></tr> |
| <tr class="memitem:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a> () override</td></tr> |
| <tr class="memdesc:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="mdescLeft"> </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"> </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="-"/> 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 </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"> </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"> </td></tr> |
| </table><table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a> |
| Static Public Member Functions</h2></td></tr> |
| <tr class="memitem:a3113fd3147c1bbc06b3f9890063c87c7"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#a3113fd3147c1bbc06b3f9890063c87c7">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> &conv_info, const <a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a> &weights_info=<a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(), const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &dilation=<a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)</td></tr> |
| <tr class="memdesc:a3113fd3147c1bbc06b3f9890063c87c7"><td class="mdescLeft"> </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_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a>. <a href="#a3113fd3147c1bbc06b3f9890063c87c7">More...</a><br /></td></tr> |
| <tr class="separator:a3113fd3147c1bbc06b3f9890063c87c7"><td class="memSeparator" colspan="2"> </td></tr> |
| </table> |
| <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> |
| <div class="textblock"><p>Basic function to compute the convolution layer. </p> |
| <p>This function calls the following OpenCL kernels/functions:</p> |
| <ol type="1"> |
| <li><a class="el" href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml">CLIm2ColKernel</a></li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml">CLGEMM</a> (if the data type is FP32 or FP16)</li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml">CLGEMMLowpMatrixMultiplyCore</a> (if the data type is QASYMM8)</li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml">CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint</a> (if the data type is QASYMM8)</li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml">CLCol2ImKernel</a> (if NCHW data layout) </li> |
| </ol> |
| |
| <p class="definition">Definition at line <a class="el" href="_c_l_g_e_m_m_convolution_layer_8h_source.xhtml#l00144">144</a> of file <a class="el" href="_c_l_g_e_m_m_convolution_layer_8h_source.xhtml">CLGEMMConvolutionLayer.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="ab8508bf65741613fe624bdeaadd7a7f8"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ab8508bf65741613fe624bdeaadd7a7f8">◆ </a></span>CLGEMMConvolutionLayer() <span class="overload">[1/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > </td> |
| <td class="paramname"><em>memory_manager</em> = <code>nullptr</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> * </td> |
| <td class="paramname"><em>weights_manager</em> = <code>nullptr</code> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Constructor. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">memory_manager</td><td>(Optional) <a class="el" href="classarm__compute_1_1_memory.xhtml" title="CPU implementation of memory object.">Memory</a> manager. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights_manager</td><td>(Optional) Weights manager. </td></tr> |
| </table> |
| </dd> |
| </dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00096">96</a> of file <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml">CLGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  : _memory_group(memory_manager), _weights_manager(weights_manager), _reshape_weights(), _reshape_weights_managed(), _im2col_kernel(), _mm_gemm(memory_manager, weights_manager),</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  _mm_gemmlowp(memory_manager), _col2im_kernel(), _activationlayer_function(), _original_weights(<span class="keyword">nullptr</span>), _im2col_output(), _weights_reshaped(), _gemm_output(), _skip_im2col(<span class="keyword">false</span>),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  _skip_col2im(<span class="keyword">false</span>), _is_quantized(<span class="keyword">false</span>), _fuse_activation(<span class="keyword">true</span>), _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a4229132541843309482de80bc7083fff"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a4229132541843309482de80bc7083fff">◆ </a></span>CLGEMMConvolutionLayer() <span class="overload">[2/3]</span></h2> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
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| </td> |
| <td class="mlabels-right"> |
| <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> |
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| </div> |
| </div> |
| <a id="aec4977cc997d5b9a644050a0059485ed"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#aec4977cc997d5b9a644050a0059485ed">◆ </a></span>CLGEMMConvolutionLayer() <span class="overload">[3/3]</span></h2> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
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| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
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| <p>Default move constructor. </p> |
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| </div> |
| </div> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a id="ac485da01d91da9e1c97a7da2b9b91ad5"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ac485da01d91da9e1c97a7da2b9b91ad5">◆ </a></span>configure()</h2> |
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| <td class="memname">void configure </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> * </td> |
| <td class="paramname"><em>input</em>, </td> |
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| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> * </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_c_l_tensor.xhtml">ICLTensor</a> * </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_c_l_tensor.xhtml">ICLTensor</a> * </td> |
| <td class="paramname"><em>output</em>, </td> |
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| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> & </td> |
| <td class="paramname"><em>conv_info</em>, </td> |
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| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a> & </td> |
| <td class="paramname"><em>weights_info</em> = <code><a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>()</code>, </td> |
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| <td class="paramkey"></td> |
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| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> & </td> |
| <td class="paramname"><em>dilation</em> = <code><a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U)</code>, </td> |
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| <td class="paramkey"></td> |
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| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> & </td> |
| <td class="paramname"><em>act_info</em> = <code><a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>()</code>, </td> |
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| <td class="paramtype">unsigned int </td> |
| <td class="paramname"><em>num_groups</em> = <code>1</code> </td> |
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| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
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| <p>Set the input and output tensors. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/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> or QASYMM8/QSYMM8_PER_CHANNEL when <code>input</code> is QASYMM8. </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: Should match <code>input</code> data type, except for input of QASYMM8 type where biases should be of S32 type. </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">weights_info</td><td>Specifies if the weights tensor has been reshaped with <a class="el" href="classarm__compute_1_1_c_l_weights_reshape_kernel.xhtml" title="OpenCL kernel to perform reshaping on the weights used by convolution and locally connected layer.">CLWeightsReshapeKernel</a>. If this is not part of the fully connected layer the weights tensor has also been transposed with <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_reshape_r_h_s_matrix_kernel.xhtml" title="OpenCL kernel to reshape the RHS matrix when performing the matrix multiplication In particular,...">CLGEMMReshapeRHSMatrixKernel</a>. Data type supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">dilation</td><td>(Optional) Dilation, in elements, across x and y. Defaults to (1, 1). </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">act_info</td><td>(Optional) Activation layer information in case of a fused activation. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">num_groups</td><td>(Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout </td></tr> |
| </table> |
| </dd> |
| </dl> |
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| <p class="definition">Definition at line <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00181">181</a> of file <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml">CLGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#a3113fd3147c1bbc06b3f9890063c87c7">CLGEMMConvolutionLayer::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  biases != <span class="keyword">nullptr</span> ? biases->info() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  output->info(),</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type();</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout();</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> <span class="keywordtype">int</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#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">const</span> <span class="keywordtype">int</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#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_kernels = <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#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_width = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_width);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_height);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_kernels = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_kernels);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">const</span> UniformQuantizationInfo iq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info().uniform();</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">const</span> UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  _is_prepared = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>.retain_internal_weights();</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  _is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type());</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  _skip_im2col = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> && kernel_width == 1 && kernel_height == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().second == 1);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  _skip_col2im = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">// Only for quantize there are few cases where we cannot fuse the activation function in GEMM</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  _fuse_activation = <span class="keyword">true</span>;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">// Set the GPU target for im2col and col2im</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  _im2col_kernel.<a class="code" href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">set_target</a>(<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().target());</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  _col2im_kernel.<a class="code" href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">set_target</a>(<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().target());</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">const</span> ICLTensor *gemm_input_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  ICLTensor *gemm_output_to_use = output;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="comment">// Get parameters from conv_info</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_x = 0;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_y = 0;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  std::tie(stride_x, stride_y) = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride();</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(idx_width),</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(idx_height),</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  kernel_width,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  kernel_height,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_weights_cols = num_kernels / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keyword">const</span> ICLTensor *biases_to_use = biases;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordtype">bool</span> append_bias = <span class="keyword">false</span>;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  ICLTensor *weights_to_use = &_weights_reshaped;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> != 1 && biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  {</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="comment">// num_groups != 1 can only be for NCHW</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="comment">// Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  biases_to_use = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  append_bias = <span class="keyword">true</span>;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>))</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  _reshape_weights_managed.<a class="code" href="classarm__compute_1_1weights__transformations_1_1_c_l_convolution_layer_reshape_weights_transform.xhtml#a8e80886a1e4295b566155c41dcdb1ed0">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">acquire</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_reshape_weights_managed));</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  _reshape_weights.<a class="code" href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#ace0b4143d0ca4435da8123ac9073e59c">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, &_weights_reshaped, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  }</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  }</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>))</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  {</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  _reshape_weights_managed.<a class="code" href="classarm__compute_1_1weights__transformations_1_1_c_l_convolution_layer_reshape_weights_transform.xhtml#a8e80886a1e4295b566155c41dcdb1ed0">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">acquire</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_reshape_weights_managed));</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  }</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  {</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  _reshape_weights.<a class="code" href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#ace0b4143d0ca4435da8123ac9073e59c">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">nullptr</span>, &_weights_reshaped, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  }</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  }</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// Create tensor to store im2col reshaped inputs</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordflow">if</span>(!_skip_im2col)</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  {</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_im2col_output);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="comment">// Configure and tune im2col. im2col output shape is auto-initialized</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  _im2col_kernel.<a class="code" href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a6bc1be5100f77f4a136d8935b06d5ac6">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &_im2col_output, Size2D(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, append_bias, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// Set quantization info</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  _im2col_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info());</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">tune_kernel_static</a>(_im2col_kernel);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">// Update GEMM input</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  gemm_input_to_use = &_im2col_output;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  }</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">// Create GEMM output tensor</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  {</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  TensorShape shape_gemm;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="comment">// If we cannot skip col2im it means we run im2col as well</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  shape_gemm = _im2col_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>();</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  shape_gemm.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, mat_weights_cols);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  shape_gemm.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, conv_w * conv_h);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// TODO(COMPMID-2078): input->clone() doesn't work with subtensors for grouped convolutions.</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  TensorInfo info_gemm(shape_gemm, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  info_gemm.set_quantization_info(output->info()->quantization_info()).<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">set_data_layout</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout());</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(info_gemm);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_gemm_output);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> </div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="comment">// Update GEMM output</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  gemm_output_to_use = &_gemm_output;</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  }</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  GEMMLowpOutputStageInfo gemmlowp_output_stage;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  gemmlowp_output_stage.type = <a class="code" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a>;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  gemmlowp_output_stage.gemmlowp_offset = 0;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="comment">// Configure output stage for quantized case</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keyword">const</span> <span class="keyword">auto</span> output_quant_info = (output->info()->total_size() == 0) ? iq_info : oq_info;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_quantized_per_channel = <a class="code" href="namespacearm__compute.xhtml#a84437d80241f6a31e1a07c231ee8e3ac">is_data_type_quantized_per_channel</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>());</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_filters = (is_quantized_per_channel) ? num_kernels : 1;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a9e8fecf2dd5d28f1f277d8636be144a5">quantization::compute_quantized_multipliers_and_shifts</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(),</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(),</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  output->info(),</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  idx_kernels,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  gemmlowp_output_stage.gemmlowp_multipliers.data(),</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  gemmlowp_output_stage.gemmlowp_shifts.data());</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0];</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordtype">int</span> min_activation = 0;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keywordtype">int</span> max_activation = 0;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">const</span> std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  };</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <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="l00345"></a><span class="lineno"> 345</span>  {</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">if</span>(supported_acts.count(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation()) != 0)</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::tie(min_activation, max_activation) = <a class="code" href="namespacearm__compute.xhtml#a28220c5eeb62bead54e759565423c8a0">get_quantized_activation_min_max</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, output_quant_info);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  }</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  _fuse_activation = <span class="keyword">false</span>;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  }</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="comment">// Set the GEMMLowp output stage info</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  gemmlowp_output_stage.gemmlowp_min_bound = min_activation;</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  gemmlowp_output_stage.gemmlowp_max_bound = max_activation;</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  }</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="comment">// Configure and tune GEMM</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="comment">// In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> gemm_3d_depth = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>) ? conv_h : 0;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  configure_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">if</span>(!_skip_im2col)</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  {</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  _im2col_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  }</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="comment">// Configure and tune Col2Im</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  _col2im_kernel.<a class="code" href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a109e7bd51a51a6aecaa2402a06deb3a7">configure</a>(gemm_output_to_use, output, Size2D(conv_w, conv_h), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">tune_kernel_static</a>(_col2im_kernel);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  }</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  {</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  }</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h),</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="stringliteral">"Output shape does not match the expected one"</span>);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordflow">if</span>(!_fuse_activation)</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  {</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">configure</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  }</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> </div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> }</div><div class="ttc" id="classarm__compute_1_1_c_l_im2_col_kernel_xhtml_a6bc1be5100f77f4a136d8935b06d5ac6"><div class="ttname"><a href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a6bc1be5100f77f4a136d8935b06d5ac6">arm_compute::CLIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_im2_col_kernel_8cpp_source.xhtml#l00295">CLIm2ColKernel.cpp:295</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca"><div class="ttname"><a href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a></div><div class="ttdoc">Quantize using a fixed point multiplication.</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="namespacearm__compute_1_1quantization_xhtml_a9e8fecf2dd5d28f1f277d8636be144a5"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a9e8fecf2dd5d28f1f277d8636be144a5">arm_compute::quantization::compute_quantized_multipliers_and_shifts</a></div><div class="ttdeci">void compute_quantized_multipliers_and_shifts(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, unsigned int idx_ofms, int32_t *output_multipliers_ptr, int32_t *output_shifts_ptr)</div><div class="ttdoc">Compute quantized per-channel multipliers and shifts.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00174">AsymmHelpers.cpp:174</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</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="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00099">CLScheduler.cpp:99</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7cb842ebfe255726066039853a4322f0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">arm_compute::test::validation::weights_info</a></div><div class="ttdeci">weights_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">BatchNormalizationLayer.cpp:196</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( )</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00597">Winograd.cpp:597</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div> |
| <div class="ttc" id="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="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer_xhtml_a3113fd3147c1bbc06b3f9890063c87c7"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#a3113fd3147c1bbc06b3f9890063c87c7">arm_compute::CLGEMMConvolutionLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info=WeightsInfo(), const Size2D &dilation=Size2D(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMConvolutionLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00396">CLGEMMConvolutionLayer.cpp:396</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00061">CLTensor.cpp:61</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a138beaeb1260b90cb03bc3f761628724"><div class="ttname"><a href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > scaled_dimensions(int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00402">Utils.cpp:402</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a78839e7861ba8ffed52ca55da2745761"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">arm_compute::TensorInfo::set_quantization_info</a></div><div class="ttdeci">ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info) override</div><div class="ttdoc">Set the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00372">TensorInfo.cpp:372</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::TensorInfo::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type used for each element of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00265">TensorInfo.h:265</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00152">Error.h:152</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a84437d80241f6a31e1a07c231ee8e3ac"><div class="ttname"><a href="namespacearm__compute.xhtml#a84437d80241f6a31e1a07c231ee8e3ac">arm_compute::is_data_type_quantized_per_channel</a></div><div class="ttdeci">bool is_data_type_quantized_per_channel(DataType dt)</div><div class="ttdoc">Check if a given data type is of per channel type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01194">Utils.h:1194</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a28220c5eeb62bead54e759565423c8a0"><div class="ttname"><a href="namespacearm__compute.xhtml#a28220c5eeb62bead54e759565423c8a0">arm_compute::get_quantized_activation_min_max</a></div><div class="ttdeci">std::pair< int32_t, int32_t > get_quantized_activation_min_max(ActivationLayerInfo act_info, DataType data_type, UniformQuantizationInfo oq_info)</div><div class="ttdoc">Returns a pair of minimum and maximum values for a quantized activation.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00478">Utils.cpp:478</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a0b0eb3235749a2909dc5a101afe59a1b"><div class="ttname"><a href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00456">Error.h:456</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a2270b3e1d20651d2d8341c858c890830"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">arm_compute::test::validation::num_groups</a></div><div class="ttdeci">const unsigned int num_groups</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">Im2Col.cpp:148</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::DataLayoutDimension::BATCHES</a></div><div class="ttdoc">batches</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ae59cb3ec4cae7835d0a0283be56ef789"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout</a></div><div class="ttdeci">src_info set_data_layout(data_layout)</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">Utils.h:1139</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a></div><div class="ttdoc">Lower and Upper Bounded Rectifier ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00127">CLTensorAllocator.cpp:127</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1weights__transformations_1_1_c_l_convolution_layer_reshape_weights_transform_xhtml_a8e80886a1e4295b566155c41dcdb1ed0"><div class="ttname"><a href="classarm__compute_1_1weights__transformations_1_1_c_l_convolution_layer_reshape_weights_transform.xhtml#a8e80886a1e4295b566155c41dcdb1ed0">arm_compute::weights_transformations::CLConvolutionLayerReshapeWeightsTransform::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *biases, unsigned int num_groups)</div><div class="ttdoc">Configures the CLConvolutionLayerReshapeWeights function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_convolution_layer_8h_source.xhtml#l00095">CLGEMMConvolutionLayer.h:95</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a></div><div class="ttdoc">Upper Bounded Rectifier ( )</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="classarm__compute_1_1_c_l_activation_layer_xhtml_a239fea32ba46d038ba350dba58026c45"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">arm_compute::CLActivationLayer::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfo act_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_8cpp_source.xhtml#l00038">CLActivationLayer.cpp:38</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_tensor_shape_xhtml_a9c54fb6cea3557692fe7c00c40bb40ad"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">arm_compute::TensorShape::set</a></div><div class="ttdeci">TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00078">TensorShape.h:78</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="classarm__compute_1_1_i_c_l_kernel_xhtml_ad5ba9d34a3a855bf1dd2e36316ff550a"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">arm_compute::ICLKernel::set_target</a></div><div class="ttdeci">void set_target(GPUTarget target)</div><div class="ttdoc">Set the targeted GPU architecture.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_kernel_8h_source.xhtml#l00271">ICLKernel.h:271</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5f5b6c4337eac9e2e0046ca2304d80dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">ArithmeticAddition.cpp:138</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_convolution_layer_reshape_weights_xhtml_ace0b4143d0ca4435da8123ac9073e59c"><div class="ttname"><a href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#ace0b4143d0ca4435da8123ac9073e59c">arm_compute::CLConvolutionLayerReshapeWeights::configure</a></div><div class="ttdeci">void configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, unsigned int num_groups=1)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00049">CLGEMMConvolutionLayer.cpp:49</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_c_l_scheduler_xhtml_a6c2059df991a75abef4eb643510c9544"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">arm_compute::CLScheduler::tune_kernel_static</a></div><div class="ttdeci">void tune_kernel_static(ICLKernel &kernel)</div><div class="ttdoc">Tunes OpenCL kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00079">CLScheduler.cpp:79</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 & 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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00075">Types.h:75</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_col2_im_kernel_xhtml_a109e7bd51a51a6aecaa2402a06deb3a7"><div class="ttname"><a href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a109e7bd51a51a6aecaa2402a06deb3a7">arm_compute::CLCol2ImKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const Size2D &convolved_dims, unsigned int num_groups=1)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_col2_im_kernel_8cpp_source.xhtml#l00091">CLCol2ImKernel.cpp:91</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a9b3e3aff065fafe490a5e190bf530ada"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">arm_compute::IWeightsManager::acquire</a></div><div class="ttdeci">ITensor * acquire(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Acquire the requested reshape tensor of the selected weights.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00117">IWeightsManager.cpp:117</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00117">IWeightsManager::acquire()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">arm_compute::test::validation::act_info</a>, <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00127">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00061">CLTensor::allocator()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager::are_weights_managed()</a>, <a class="el" href="_error_8h_source.xhtml#l00456">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00455">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_error_8h_source.xhtml#l00152">ARM_COMPUTE_UNUSED</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::BATCHES</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::BOUNDED_RELU</a>, <a class="el" href="_asymm_helpers_8cpp_source.xhtml#l00174">arm_compute::quantization::compute_quantized_multipliers_and_shifts()</a>, <a class="el" href="_c_l_activation_layer_8cpp_source.xhtml#l00038">CLActivationLayer::configure()</a>, <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00049">CLConvolutionLayerReshapeWeights::configure()</a>, <a class="el" href="_c_l_col2_im_kernel_8cpp_source.xhtml#l00091">CLCol2ImKernel::configure()</a>, <a class="el" href="_c_l_im2_col_kernel_8cpp_source.xhtml#l00295">CLIm2ColKernel::configure()</a>, <a class="el" href="_c_l_g_e_m_m_convolution_layer_8h_source.xhtml#l00095">CLConvolutionLayerReshapeWeightsTransform::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="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">arm_compute::test::validation::data_layout</a>, <a class="el" href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">arm_compute::test::validation::data_type</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00265">TensorInfo::data_type()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::dilation</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00232">TensorInfo::dimension()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01951">GEMMLowpOutputStageInfo::gemmlowp_max_bound</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01950">GEMMLowpOutputStageInfo::gemmlowp_min_bound</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01948">GEMMLowpOutputStageInfo::gemmlowp_multiplier</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01952">GEMMLowpOutputStageInfo::gemmlowp_multipliers</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01947">GEMMLowpOutputStageInfo::gemmlowp_offset</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01949">GEMMLowpOutputStageInfo::gemmlowp_shift</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01953">GEMMLowpOutputStageInfo::gemmlowp_shifts</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00099">CLScheduler::get()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00478">arm_compute::get_quantized_activation_min_max()</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="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor::info()</a>, <a class="el" href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator::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="arm__compute_2core_2_utils_8h_source.xhtml#l01139">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01194">arm_compute::is_data_type_quantized_per_channel()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01954">GEMMLowpOutputStageInfo::is_quantized_per_channel</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::LU_BOUNDED_RELU</a>, <a class="el" href="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::NHWC</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">arm_compute::test::validation::num_groups</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::QUANTIZE_DOWN_FIXEDPOINT</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::RELU</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00402">arm_compute::scaled_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, <a class="el" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout()</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00372">TensorInfo::set_quantization_info()</a>, <a class="el" href="_i_c_l_kernel_8h_source.xhtml#l00271">ICLKernel::set_target()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00261">TensorInfo::tensor_shape()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">ITensorInfo::total_size()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00079">CLScheduler::tune_kernel_static()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01946">GEMMLowpOutputStageInfo::type</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00148">QuantizationInfo::uniform()</a>, <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00396">CLGEMMConvolutionLayer::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="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">arm_compute::test::validation::weights_info</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p> |
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| </div> |
| </div> |
| <a id="a025e6bce0640a6cb5ecdab8a3e57f9a0"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a025e6bce0640a6cb5ecdab8a3e57f9a0">◆ </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_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
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| </table> |
| </td> |
| <td class="mlabels-right"> |
| <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> |
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| </div> |
| </div> |
| <a id="a78f1fff174957ab8dd876ee696d5a749"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a78f1fff174957ab8dd876ee696d5a749">◆ </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_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
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| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
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| </div><div class="memdoc"> |
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| <p>Default move assignment operator. </p> |
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| </div> |
| </div> |
| <a id="aa9b93ef660fc3c5b4b19d3fc7b891b77"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">◆ </a></span>prepare()</h2> |
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| <td class="memname">void prepare </td> |
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| <td class="paramname"></td><td>)</td> |
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| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </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> |
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| <p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p> |
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| <p class="definition">Definition at line <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00629">629</a> of file <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml">CLGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> {</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  {</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(_original_weights))</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  {</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">run</a>(_original_weights, &_reshape_weights_managed);</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  }</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  {</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="comment">// Run weights reshaping and mark original weights tensor as unused</span></div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  _weights_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  _reshape_weights.<a class="code" href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  _original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  }</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span> </div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="comment">// Prepare GEMM</span></div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  _is_quantized ? _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>() : _mm_gemm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keywordflow">if</span>(!_weights_reshaped.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>())</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  {</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  _weights_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  }</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a398a2582c746d28fc125487a44c9ed74">queue</a>().finish();</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  }</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> }</div><div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLGEMM::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="_c_l_g_e_m_m_8cpp_source.xhtml#l00720">CLGEMM.cpp:720</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLGEMMLowpMatrixMultiplyCore::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="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00496">CLGEMMLowpMatrixMultiplyCore.cpp:496</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00099">CLScheduler.cpp:99</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00466">Error.h:466</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00061">CLTensor.cpp:61</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_i_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a398a2582c746d28fc125487a44c9ed74"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a398a2582c746d28fc125487a44c9ed74">arm_compute::CLScheduler::queue</a></div><div class="ttdeci">cl::CommandQueue & queue()</div><div class="ttdoc">Accessor for the associated CL command queue.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00127">CLTensorAllocator.cpp:127</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::CLTensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00159">CLTensorAllocator.cpp:159</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a73808ac61e51d72c7d6349d6d51dcf37"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">arm_compute::IWeightsManager::run</a></div><div class="ttdeci">ITensor * run(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Run the reshape function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00051">IWeightsManager.cpp:51</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_convolution_layer_reshape_weights_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLConvolutionLayerReshapeWeights::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="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00091">CLGEMMConvolutionLayer.cpp:91</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00127">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00061">CLTensor::allocator()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager::are_weights_managed()</a>, <a class="el" href="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00159">CLTensorAllocator::free()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00099">CLScheduler::get()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00162">ITensor::is_used()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00496">CLGEMMLowpMatrixMultiplyCore::prepare()</a>, <a class="el" href="_c_l_g_e_m_m_8cpp_source.xhtml#l00720">CLGEMM::prepare()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler::queue()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00051">IWeightsManager::run()</a>, and <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00091">CLConvolutionLayerReshapeWeights::run()</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00592">CLGEMMConvolutionLayer::run()</a>.</p> |
| |
| </div> |
| </div> |
| <a id="ad1717410afd0be936c6213a63c8005fb"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">◆ </a></span>run()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
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| <td class="memname">void run </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
| </tr> |
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| </div><div class="memdoc"> |
| |
| <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_c_l_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl> |
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| <p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p> |
| |
| <p class="definition">Definition at line <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00592">592</a> of file <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml">CLGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> {</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> </div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span> </div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="comment">// Run im2col</span></div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keywordflow">if</span>(!_skip_im2col)</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  {</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_im2col_kernel);</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  }</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> </div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="comment">// Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  {</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="comment">// Run gemmlowp</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  }</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  {</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="comment">// Run gemm</span></div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  }</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> </div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="comment">// Reshape output matrix</span></div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  {</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_col2im_kernel, <span class="keyword">false</span>);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  }</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> </div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="comment">//Run Activation Layer if we cannot fuse in GEMM</span></div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="keywordflow">if</span>(!_fuse_activation)</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  {</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  }</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> }</div><div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMM::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="_c_l_g_e_m_m_8cpp_source.xhtml#l00639">CLGEMM.cpp:639</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00099">CLScheduler.cpp:99</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMMLowpMatrixMultiplyCore::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="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00440">CLGEMMLowpMatrixMultiplyCore.cpp:440</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::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_c_l_simple_function_8cpp_source.xhtml#l00039">ICLSimpleFunction.cpp:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ae1a643e517f50bf0392fb6516dd7cf67"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">arm_compute::CLScheduler::enqueue</a></div><div class="ttdeci">void enqueue(ICLKernel &kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00154">CLScheduler.cpp:154</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLGEMMConvolutionLayer::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="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00629">CLGEMMConvolutionLayer.cpp:629</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00154">CLScheduler::enqueue()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00099">CLScheduler::get()</a>, <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00629">CLGEMMConvolutionLayer::prepare()</a>, <a class="el" href="_i_c_l_simple_function_8cpp_source.xhtml#l00039">ICLSimpleFunction::run()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00440">CLGEMMLowpMatrixMultiplyCore::run()</a>, and <a class="el" href="_c_l_g_e_m_m_8cpp_source.xhtml#l00639">CLGEMM::run()</a>.</p> |
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| </div> |
| </div> |
| <a id="a3113fd3147c1bbc06b3f9890063c87c7"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a3113fd3147c1bbc06b3f9890063c87c7">◆ </a></span>validate()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_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> * </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> * </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> * </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> * </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> & </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_weights_info.xhtml">WeightsInfo</a> & </td> |
| <td class="paramname"><em>weights_info</em> = <code><a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>()</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> & </td> |
| <td class="paramname"><em>dilation</em> = <code><a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U)</code>, </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> & </td> |
| <td class="paramname"><em>act_info</em> = <code><a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>()</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">unsigned int </td> |
| <td class="paramname"><em>num_groups</em> = <code>1</code> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <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_c_l_g_e_m_m_convolution_layer.xhtml">CLGEMMConvolutionLayer</a>. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/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> or QASYMM8/QSYMM8_PER_CHANNEL when <code>input</code> is QASYMM8. </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: Should match <code>input</code> data type, except for input of QASYMM8 type where biases should be of S32 type. </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">weights_info</td><td>Specifies if the weights tensor has been reshaped with <a class="el" href="classarm__compute_1_1_c_l_weights_reshape_kernel.xhtml" title="OpenCL kernel to perform reshaping on the weights used by convolution and locally connected layer.">CLWeightsReshapeKernel</a>. If this is not part of the fully connected layer the weights tensor has also been transposed with <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_reshape_r_h_s_matrix_kernel.xhtml" title="OpenCL kernel to reshape the RHS matrix when performing the matrix multiplication In particular,...">CLGEMMReshapeRHSMatrixKernel</a>. Data type supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">dilation</td><td>(Optional) Dilation, in elements, across x and y. Defaults to (1, 1). </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">act_info</td><td>(Optional) Activation layer information in case of a fused activation. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">num_groups</td><td>(Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout</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="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00396">396</a> of file <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml">CLGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> {</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>.are_reshaped(), <span class="stringliteral">"Weights already reshaped are not supported!"</span>);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <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#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">DataType::QASYMM8_SIGNED</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_quantized_per_channel = <a class="code" href="namespacearm__compute.xhtml#a84437d80241f6a31e1a07c231ee8e3ac">is_data_type_quantized_per_channel</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->data_type());</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">if</span>(is_quantized_per_channel)</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_type() != <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <span class="stringliteral">"Input data type not compatible with Weights"</span>);</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  }</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  {</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <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="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</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="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> != 1) && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout() != <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>), <span class="stringliteral">"Grouping (num_groups != 1) with NHWC data layout is not supported"</span>);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> != 1) && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_type() == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>), <span class="stringliteral">"Grouping (num_groups != 1) is not supported with QASYMM8"</span>);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <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>->dimension(2) / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(2)) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>) && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>));</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout();</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_type();</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">const</span> <span class="keywordtype">int</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#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">const</span> <span class="keywordtype">int</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#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_channel = <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#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_kernels = <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#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_width = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_width);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_height);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_kernels = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_kernels);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  TensorInfo im2col_reshaped_info{};</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  TensorInfo info_gemm{};</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  TensorInfo weights_reshaped_info{};</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">const</span> ITensorInfo *gemm_input_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">const</span> ITensorInfo *gemm_output_to_use = output;</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">const</span> ITensorInfo *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> skip_im2col = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> && kernel_width == 1 && kernel_height == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().second == 1);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> skip_col2im = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keywordtype">bool</span> fuse_activation = <span class="keyword">true</span>;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_channel) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_channel));</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->num_dimensions() > 4);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> </div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="comment">// Validate biases</span></div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  {</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  {</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(biases, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  }</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  {</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <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="l00452"></a><span class="lineno"> 452</span>  }</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases->dimension(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_kernels));</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases->num_dimensions() > 1);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  }</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <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="l00458"></a><span class="lineno"> 458</span>  {</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.b() > <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.a());</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  }</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_width),</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_height),</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  kernel_width,</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  kernel_height,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_weights_cols = num_kernels / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keyword">const</span> ITensorInfo *biases_to_use = biases;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordtype">bool</span> append_bias = <span class="keyword">false</span>;</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> != 1 && biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  {</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="comment">// num_groups != 1 can only be for NCHW</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="comment">// Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  biases_to_use = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  append_bias = <span class="keyword">true</span>;</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#a25364a94d15ee892bf2db2a37fa67be6">CLConvolutionLayerReshapeWeights::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  weights_reshaped_info = TensorInfo(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49">compute_weights_reshaped_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">true</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>), 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  }</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  {</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#a25364a94d15ee892bf2db2a37fa67be6">CLConvolutionLayerReshapeWeights::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">nullptr</span>, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  weights_reshaped_info = TensorInfo(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49">compute_weights_reshaped_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">false</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>), 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  }</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  weights_to_use = &weights_reshaped_info;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keywordflow">if</span>(!skip_im2col)</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  {</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keyword">const</span> Size2D kernel_dims(kernel_width, kernel_height);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="comment">// Output tensor auto initialization if not yet initialized</span></div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  TensorShape expected_output_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7">compute_im2col_conv_shape</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, append_bias, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> == 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(im2col_reshaped_info, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->clone()->set_tensor_shape(expected_output_shape));</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">CLIm2ColKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &im2col_reshaped_info, kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, append_bias, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  gemm_input_to_use = &im2col_reshaped_info;</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  }</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> </div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="comment">// Create GEMM output tensor</span></div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keywordflow">if</span>(!skip_col2im)</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  {</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  TensorShape shape_gemm;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  shape_gemm = gemm_input_to_use->tensor_shape();</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  shape_gemm.set(0, mat_weights_cols);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  shape_gemm.set(1, conv_w * conv_h);</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  info_gemm = TensorInfo(shape_gemm, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  info_gemm.set_quantization_info(output->quantization_info()).<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">set_data_layout</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout());</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  gemm_output_to_use = &info_gemm;</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  }</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  GEMMLowpOutputStageInfo gemmlowp_output_stage;</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  gemmlowp_output_stage.type = <a class="code" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a>;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  gemmlowp_output_stage.gemmlowp_offset = 0;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> </div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  {</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keyword">const</span> UniformQuantizationInfo iq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->quantization_info().uniform();</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keyword">const</span> UniformQuantizationInfo oq_info = output->quantization_info().uniform();</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keyword">const</span> <span class="keyword">auto</span> output_quant_info = (output->total_size() == 0) ? iq_info : oq_info;</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_filters = (is_quantized_per_channel) ? num_kernels : 1;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> </div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a9e8fecf2dd5d28f1f277d8636be144a5">quantization::compute_quantized_multipliers_and_shifts</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>,</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>,</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  output,</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  idx_kernels,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  gemmlowp_output_stage.gemmlowp_multipliers.data(),</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  gemmlowp_output_stage.gemmlowp_shifts.data());</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0];</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span> </div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keywordtype">int</span> min_activation = 0;</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keywordtype">int</span> max_activation = 0;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> </div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keyword">const</span> std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>,</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a></div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  };</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> </div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <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="l00555"></a><span class="lineno"> 555</span>  {</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordflow">if</span>(supported_acts.count(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation()) != 0)</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  {</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  std::tie(min_activation, max_activation) = <a class="code" href="namespacearm__compute.xhtml#a28220c5eeb62bead54e759565423c8a0">get_quantized_activation_min_max</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, output_quant_info);</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  }</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  {</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  fuse_activation = <span class="keyword">false</span>;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  }</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="comment">// Set the GEMMLowp output stage info</span></div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  gemmlowp_output_stage.gemmlowp_min_bound = min_activation;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  gemmlowp_output_stage.gemmlowp_max_bound = max_activation;</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  }</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="comment">// In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> gemm_3d_depth = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>) ? conv_h : 0;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>));</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="comment">// Validate Col2Im</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">if</span>(!skip_col2im)</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  {</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a1877ad9505323d75f1a301f30a528ab4">CLCol2ImKernel::validate</a>(gemm_output_to_use, output, Size2D(conv_w, conv_h), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  }</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> </div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="comment">//Validate Activation Layer</span></div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keywordflow">if</span>(!fuse_activation)</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  {</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::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="l00587"></a><span class="lineno"> 587</span>  }</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> </div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> }</div><div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::CLActivationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLActivationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_8cpp_source.xhtml#l00047">CLActivationLayer.cpp:47</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca"><div class="ttname"><a href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a></div><div class="ttdoc">Quantize using a fixed point multiplication.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a9e8fecf2dd5d28f1f277d8636be144a5"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a9e8fecf2dd5d28f1f277d8636be144a5">arm_compute::quantization::compute_quantized_multipliers_and_shifts</a></div><div class="ttdeci">void compute_quantized_multipliers_and_shifts(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, unsigned int idx_ofms, int32_t *output_multipliers_ptr, int32_t *output_shifts_ptr)</div><div class="ttdoc">Compute quantized per-channel multipliers and shifts.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00174">AsymmHelpers.cpp:174</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</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_1_1test_1_1validation_xhtml_a7cb842ebfe255726066039853a4322f0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">arm_compute::test::validation::weights_info</a></div><div class="ttdeci">weights_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">BatchNormalizationLayer.cpp:196</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_abdb9168800c70e5e2c4c020a3b905738"><div class="ttname"><a href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00494">Validate.h:494</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="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( )</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="_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_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="_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_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 &info, const TensorShape &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="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_col2_im_kernel_xhtml_a1877ad9505323d75f1a301f30a528ab4"><div class="ttname"><a href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a1877ad9505323d75f1a301f30a528ab4">arm_compute::CLCol2ImKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &convolved_dims, unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLCol2ImKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_col2_im_kernel_8cpp_source.xhtml#l00134">CLCol2ImKernel.cpp:134</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a138beaeb1260b90cb03bc3f761628724"><div class="ttname"><a href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > scaled_dimensions(int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00402">Utils.cpp:402</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_im2_col_kernel_xhtml_a4e256965ba7798ffe1358469be661e5a"><div class="ttname"><a href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">arm_compute::CLIm2ColKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLIm2ColKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_im2_col_kernel_8cpp_source.xhtml#l00344">CLIm2ColKernel.cpp:344</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a84437d80241f6a31e1a07c231ee8e3ac"><div class="ttname"><a href="namespacearm__compute.xhtml#a84437d80241f6a31e1a07c231ee8e3ac">arm_compute::is_data_type_quantized_per_channel</a></div><div class="ttdeci">bool is_data_type_quantized_per_channel(DataType dt)</div><div class="ttdoc">Check if a given data type is of per channel type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01194">Utils.h:1194</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a28220c5eeb62bead54e759565423c8a0"><div class="ttname"><a href="namespacearm__compute.xhtml#a28220c5eeb62bead54e759565423c8a0">arm_compute::get_quantized_activation_min_max</a></div><div class="ttdeci">std::pair< int32_t, int32_t > get_quantized_activation_min_max(ActivationLayerInfo act_info, DataType data_type, UniformQuantizationInfo oq_info)</div><div class="ttdoc">Returns a pair of minimum and maximum values for a quantized activation.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00478">Utils.cpp:478</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a2270b3e1d20651d2d8341c858c890830"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">arm_compute::test::validation::num_groups</a></div><div class="ttdeci">const unsigned int num_groups</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">Im2Col.cpp:148</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_xhtml_a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::DataLayoutDimension::BATCHES</a></div><div class="ttdoc">batches</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_1_1test_1_1validation_xhtml_ae59cb3ec4cae7835d0a0283be56ef789"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout</a></div><div class="ttdeci">src_info set_data_layout(data_layout)</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">Utils.h:1139</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_convolution_layer_reshape_weights_xhtml_a25364a94d15ee892bf2db2a37fa67be6"><div class="ttname"><a href="classarm__compute_1_1_c_l_convolution_layer_reshape_weights.xhtml#a25364a94d15ee892bf2db2a37fa67be6">arm_compute::CLConvolutionLayerReshapeWeights::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLConvolutionLayerReshap...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00066">CLGEMMConvolutionLayer.cpp:66</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a></div><div class="ttdoc">Lower and Upper Bounded Rectifier ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a></div><div class="ttdoc">Upper Bounded Rectifier ( )</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a6365b505b5c1b98916425bc692b6ea49"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49">arm_compute::misc::shape_calculator::compute_weights_reshaped_shape</a></div><div class="ttdeci">TensorShape compute_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias=false, unsigned int num_groups=1)</div><div class="ttdoc">Calculate the reshaped shape of the weights.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00149">ShapeCalculator.h:149</a></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_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a1c69762a42ab8add645d0a949b6f4b1f"><div class="ttname"><a href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="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_ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">arm_compute::DataType::QASYMM8_SIGNED</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number signed</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5f5b6c4337eac9e2e0046ca2304d80dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">ArithmeticAddition.cpp:138</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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00075">Types.h:75</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8a9286d053e9f3a958064e4f3cdd02f7"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7">arm_compute::misc::shape_calculator::compute_im2col_conv_shape</a></div><div class="ttdeci">TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, bool batch_size_on_z, unsigned int num_groups=1)</div><div class="ttdoc">Calculate the im2col output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00549">ShapeCalculator.h:549</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#l00466">ARM_COMPUTE_ERROR_ON</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#l00494">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</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#l00244">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>, <a class="el" href="_validate_8h_source.xhtml#l00163">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00202">arm_compute::auto_init_if_empty()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::BATCHES</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::BOUNDED_RELU</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00549">arm_compute::misc::shape_calculator::compute_im2col_conv_shape()</a>, <a class="el" href="_asymm_helpers_8cpp_source.xhtml#l00174">arm_compute::quantization::compute_quantized_multipliers_and_shifts()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00149">arm_compute::misc::shape_calculator::compute_weights_reshaped_shape()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">arm_compute::test::validation::data_layout</a>, <a class="el" href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">arm_compute::test::validation::data_type</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::dilation</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01951">GEMMLowpOutputStageInfo::gemmlowp_max_bound</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01950">GEMMLowpOutputStageInfo::gemmlowp_min_bound</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01948">GEMMLowpOutputStageInfo::gemmlowp_multiplier</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01952">GEMMLowpOutputStageInfo::gemmlowp_multipliers</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01947">GEMMLowpOutputStageInfo::gemmlowp_offset</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01949">GEMMLowpOutputStageInfo::gemmlowp_shift</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01953">GEMMLowpOutputStageInfo::gemmlowp_shifts</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00478">arm_compute::get_quantized_activation_min_max()</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="arm__compute_2core_2_utils_8h_source.xhtml#l01139">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01194">arm_compute::is_data_type_quantized_per_channel()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01954">GEMMLowpOutputStageInfo::is_quantized_per_channel</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::LU_BOUNDED_RELU</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="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">arm_compute::test::validation::num_groups</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::QASYMM8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">arm_compute::QASYMM8_SIGNED</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::QUANTIZE_DOWN_FIXEDPOINT</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::RELU</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00402">arm_compute::scaled_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, <a class="el" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout()</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="arm__compute_2core_2_types_8h_source.xhtml#l01946">GEMMLowpOutputStageInfo::type</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00148">QuantizationInfo::uniform()</a>, <a class="el" href="_c_l_activation_layer_8cpp_source.xhtml#l00047">CLActivationLayer::validate()</a>, <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00066">CLConvolutionLayerReshapeWeights::validate()</a>, <a class="el" href="_c_l_col2_im_kernel_8cpp_source.xhtml#l00134">CLCol2ImKernel::validate()</a>, <a class="el" href="_c_l_im2_col_kernel_8cpp_source.xhtml#l00344">CLIm2ColKernel::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="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">arm_compute::test::validation::weights_info</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00181">CLGEMMConvolutionLayer::configure()</a>, and <a class="el" href="_c_l_convolution_layer_8cpp_source.xhtml#l00092">CLConvolutionLayer::validate()</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following files:<ul> |
| <li>arm_compute/runtime/CL/functions/<a class="el" href="_c_l_g_e_m_m_convolution_layer_8h_source.xhtml">CLGEMMConvolutionLayer.h</a></li> |
| <li>src/runtime/CL/functions/<a class="el" href="_c_l_g_e_m_m_convolution_layer_8cpp_source.xhtml">CLGEMMConvolutionLayer.cpp</a></li> |
| </ul> |
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