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|  <span id="projectnumber">20.02.1</span> |
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| <div class="title">GCConvolutionLayer Class Reference</div> </div> |
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| |
| <p>Basic function to compute the convolution layer. |
| <a href="classarm__compute_1_1_g_c_convolution_layer.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_g_c_convolution_layer_8h_source.xhtml">GCConvolutionLayer.h</a>></code></p> |
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| Collaboration diagram for GCConvolutionLayer:</div> |
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| <table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> |
| Public Member Functions</h2></td></tr> |
| <tr class="memitem:a3f5eb5b7c01f8912b75ed6703327546d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#a3f5eb5b7c01f8912b75ed6703327546d">GCConvolutionLayer</a> (std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > memory_manager=nullptr)</td></tr> |
| <tr class="memdesc:a3f5eb5b7c01f8912b75ed6703327546d"><td class="mdescLeft"> </td><td class="mdescRight">Default constructor. <a href="#a3f5eb5b7c01f8912b75ed6703327546d">More...</a><br /></td></tr> |
| <tr class="separator:a3f5eb5b7c01f8912b75ed6703327546d"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a3509ee3d42daede26e171278011a99bd"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#a3509ee3d42daede26e171278011a99bd">GCConvolutionLayer</a> (const <a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a3509ee3d42daede26e171278011a99bd"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a3509ee3d42daede26e171278011a99bd">More...</a><br /></td></tr> |
| <tr class="separator:a3509ee3d42daede26e171278011a99bd"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a794be2d2a448608aa574d113ea38d5c1"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#a794be2d2a448608aa574d113ea38d5c1">GCConvolutionLayer</a> (<a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a794be2d2a448608aa574d113ea38d5c1"><td class="mdescLeft"> </td><td class="mdescRight">Default move constructor. <a href="#a794be2d2a448608aa574d113ea38d5c1">More...</a><br /></td></tr> |
| <tr class="separator:a794be2d2a448608aa574d113ea38d5c1"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a87ed9138ded739663871c103fc77b36a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#a87ed9138ded739663871c103fc77b36a">operator=</a> (const <a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a87ed9138ded739663871c103fc77b36a"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a87ed9138ded739663871c103fc77b36a">More...</a><br /></td></tr> |
| <tr class="separator:a87ed9138ded739663871c103fc77b36a"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a0a2e80b84845bf2538dd5770089913b7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#a0a2e80b84845bf2538dd5770089913b7">operator=</a> (<a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a0a2e80b84845bf2538dd5770089913b7"><td class="mdescLeft"> </td><td class="mdescRight">Default move assignment operator. <a href="#a0a2e80b84845bf2538dd5770089913b7">More...</a><br /></td></tr> |
| <tr class="separator:a0a2e80b84845bf2538dd5770089913b7"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a9d4bf10fbda1b7ca0b4c205512dc5a93"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#a9d4bf10fbda1b7ca0b4c205512dc5a93">configure</a> (const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &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:a9d4bf10fbda1b7ca0b4c205512dc5a93"><td class="mdescLeft"> </td><td class="mdescRight">Set the input and output tensors. <a href="#a9d4bf10fbda1b7ca0b4c205512dc5a93">More...</a><br /></td></tr> |
| <tr class="separator:a9d4bf10fbda1b7ca0b4c205512dc5a93"><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_g_c_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_g_c_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> |
| <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 GLES kernels:</p> |
| <ol type="1"> |
| <li><a class="el" href="classarm__compute_1_1_g_c_weights_reshape_kernel.xhtml">GCWeightsReshapeKernel</a> (executed only once for each configuration)</li> |
| <li><a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_transpose1x_w_kernel.xhtml">GCGEMMTranspose1xWKernel</a> (executed only once for each configuration)</li> |
| <li><a class="el" href="classarm__compute_1_1_g_c_im2_col_kernel.xhtml">GCIm2ColKernel</a></li> |
| <li><a class="el" href="classarm__compute_1_1_g_c_g_e_m_m_interleave4x4_kernel.xhtml">GCGEMMInterleave4x4Kernel</a></li> |
| <li><a class="el" href="classarm__compute_1_1_g_c_col2_im_kernel.xhtml">GCCol2ImKernel</a> </li> |
| </ol> |
| |
| <p class="definition">Definition at line <a class="el" href="_g_c_convolution_layer_8h_source.xhtml#l00076">76</a> of file <a class="el" href="_g_c_convolution_layer_8h_source.xhtml">GCConvolutionLayer.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="a3f5eb5b7c01f8912b75ed6703327546d"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a3f5eb5b7c01f8912b75ed6703327546d">◆ </a></span>GCConvolutionLayer() <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_g_c_convolution_layer.xhtml">GCConvolutionLayer</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><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Default constructor. </p> |
| |
| <p class="definition">Definition at line <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml#l00069">69</a> of file <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml">GCConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _mm_gemm(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), _original_weights(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _is_activationlayer_enabled(<span class="keyword">false</span>), _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a3509ee3d42daede26e171278011a99bd"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a3509ee3d42daede26e171278011a99bd">◆ </a></span>GCConvolutionLayer() <span class="overload">[2/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
| |
| </div> |
| </div> |
| <a id="a794be2d2a448608aa574d113ea38d5c1"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a794be2d2a448608aa574d113ea38d5c1">◆ </a></span>GCConvolutionLayer() <span class="overload">[3/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Default move constructor. </p> |
| |
| </div> |
| </div> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a id="a9d4bf10fbda1b7ca0b4c205512dc5a93"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a9d4bf10fbda1b7ca0b4c205512dc5a93">◆ </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_g_c_tensor.xhtml">IGCTensor</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_g_c_tensor.xhtml">IGCTensor</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_g_c_tensor.xhtml">IGCTensor</a> * </td> |
| <td class="paramname"><em>biases</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</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> |
| </div><div class="memdoc"> |
| |
| <p>Set the input and output tensors. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: F16/F32. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: 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_g_c_weights_reshape_kernel.xhtml" title="GLES Compute kernel to perform reshaping on the weights used by convolution and locally connected lay...">GCWeightsReshapeKernel</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_g_c_g_e_m_m_transpose1x_w_kernel.xhtml" title="OpenGLES kernel which transposes the elements of a matrix in chunks of 1xW, where W is equal to (16 /...">GCGEMMTranspose1xWKernel</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 not supported </td></tr> |
| </table> |
| </dd> |
| </dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml#l00090">90</a> of file <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml">GCConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</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>);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_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#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_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="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_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="l00097"></a><span class="lineno"> 97</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#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>(2) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(2));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</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#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#a13622133d9b41900a6a3e8f89e59a78b">num_dimensions</a>() > 4);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</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#a2270b3e1d20651d2d8341c858c890830">num_groups</a> > 1);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_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="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(biases-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><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>(3));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(biases-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dt = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// Set the GPU target for im2col and col2im</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  _input_im2col_kernel.<a class="code" href="classarm__compute_1_1_i_g_c_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">set_target</a>(<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().get_target());</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  _output_col2im_kernel.<a class="code" href="classarm__compute_1_1_i_g_c_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">set_target</a>(<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().get_target());</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> append_bias = (biases != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> bias_element = (append_bias) ? 1 : 0;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *biases_to_use = (append_bias) ? biases : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// Get parameters from conv_info</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_x = 0;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_y = 0;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</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="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</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>(0);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</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>(1);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</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(0), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(1), kernel_width, kernel_height,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_weights_cols = <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>(3);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_weights_rows = <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>(0) * <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>(1) * <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>(2) + bias_element;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// _weights_reshaped will be auto configured in the kernel.</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// Just append biases and do not transpose 1xW as it will be reshaped in GCGEMM</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  _reshape_weights.<a class="code" href="classarm__compute_1_1_g_c_convolution_layer_reshape_weights.xhtml#a35aacc414eddf01fa5ae44483c110a33">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases_to_use, &_weights_reshaped);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = &_weights_reshaped;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="comment">// Create tensor to store im2col reshaped inputs</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_input_cols = mat_weights_rows;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_input_rows = conv_w * conv_h;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape();</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, mat_input_cols);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, mat_input_rows);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(2, 1);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="comment">// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> im2col_reshaped_info(shape_im2col, 1, dt);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  _input_im2col_reshaped.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(im2col_reshaped_info);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_input_im2col_reshaped);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// Create GEMM output tensor</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_gemm = _input_im2col_reshaped.<a class="code" href="classarm__compute_1_1_g_c_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="l00160"></a><span class="lineno"> 160</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="l00161"></a><span class="lineno"> 161</span>  shape_gemm.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, mat_input_rows);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> gemm_data_type = dt;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="comment">// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> info_gemm(shape_gemm, 1, gemm_data_type);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">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="l00167"></a><span class="lineno"> 167</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="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">if</span>(dt == <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a> border_size = <a class="code" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left());</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->extend_padding(border_size);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  _fill_border.<a class="code" href="classarm__compute_1_1_g_c_fill_border_kernel.xhtml#a148acc5bac0dddc8d512b4d91bd2a7ba">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, border_size, <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>, <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a>()); <span class="comment">// for PAD of im2col fp16: consider it as border</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="comment">// Configure im2col</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  _input_im2col_kernel.<a class="code" href="classarm__compute_1_1_g_c_im2_col_kernel.xhtml#a2461b3d633deab1e051da8170c959b2a">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &_input_im2col_reshaped, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, append_bias, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// Configure GEMM</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  configure_mm(&_input_im2col_reshaped, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_gemm_output);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  _input_im2col_reshaped.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// Configure Col2Im</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  _output_col2im_kernel.<a class="code" href="classarm__compute_1_1_g_c_col2_im_kernel.xhtml#a1aebc8a9a10fb43c9a39d241d0c11338">configure</a>(&_gemm_output, output, std::make_pair(conv_w, conv_h));</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>((output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != conv_w) || (output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) != conv_h), <span class="stringliteral">"Output shape does not match the expected one"</span>);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">//Configure Activation Layer</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  _is_activationlayer_enabled = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled();</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_g_c_activation_layer.xhtml#a0fdcd48f36eb1310d56f0f0d5ce9ab00">configure</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</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="l00198"></a><span class="lineno"> 198</span> }</div><div class="ttc" id="classarm__compute_1_1_g_c_im2_col_kernel_xhtml_a2461b3d633deab1e051da8170c959b2a"><div class="ttname"><a href="classarm__compute_1_1_g_c_im2_col_kernel.xhtml#a2461b3d633deab1e051da8170c959b2a">arm_compute::GCIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, IGCTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_im2_col_kernel_8cpp_source.xhtml#l00067">GCIm2ColKernel.cpp:67</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div> |
| <div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml">arm_compute::PixelValue</a></div><div class="ttdoc">Class describing the value of a pixel for any image format.</div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00034">PixelValue.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor.cpp:41</a></div></div> |
| <div class="ttc" id="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_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a5befbfaf6bc224eabc58b5e88b1de6d1"><div class="ttname"><a href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00543">Validate.h:543</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml">arm_compute::BorderSize</a></div><div class="ttdoc">Container for 2D border size.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00269">Types.h:269</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="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_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="_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_i_g_c_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_g_c_tensor.xhtml">arm_compute::IGCTensor</a></div><div class="ttdoc">Interface for GLES Compute tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_g_c_tensor_8h_source.xhtml#l00035">IGCTensor.h:35</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_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="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_i_g_c_kernel_xhtml_ad5ba9d34a3a855bf1dd2e36316ff550a"><div class="ttname"><a href="classarm__compute_1_1_i_g_c_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">arm_compute::IGCKernel::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_g_c_kernel_8h_source.xhtml#l00113">IGCKernel.h:113</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a9c5f715748222ab9607cc52134b36b0b"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">arm_compute::GCScheduler::get</a></div><div class="ttdeci">static GCScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00070">GCScheduler.cpp:70</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a13622133d9b41900a6a3e8f89e59a78b"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b">arm_compute::TensorInfo::num_dimensions</a></div><div class="ttdeci">size_t num_dimensions() const override</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00253">TensorInfo.h:253</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_convolution_layer_reshape_weights_xhtml_a35aacc414eddf01fa5ae44483c110a33"><div class="ttname"><a href="classarm__compute_1_1_g_c_convolution_layer_reshape_weights.xhtml#a35aacc414eddf01fa5ae44483c110a33">arm_compute::GCConvolutionLayerReshapeWeights::configure</a></div><div class="ttdeci">void configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_convolution_layer_8cpp_source.xhtml#l00044">GCConvolutionLayer.cpp:44</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="_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="classarm__compute_1_1_g_c_fill_border_kernel_xhtml_a148acc5bac0dddc8d512b4d91bd2a7ba"><div class="ttname"><a href="classarm__compute_1_1_g_c_fill_border_kernel.xhtml#a148acc5bac0dddc8d512b4d91bd2a7ba">arm_compute::GCFillBorderKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *tensor, BorderSize border_size, BorderMode border_mode, const PixelValue &constant_border_value=PixelValue())</div><div class="ttdoc">Initialise the kernel's input, output and border mode.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_fill_border_kernel_8cpp_source.xhtml#l00060">GCFillBorderKernel.cpp:60</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_aa8a4946cd749d482dd996874d295af85"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">arm_compute::ITensorAllocator::allocate</a></div><div class="ttdeci">virtual void allocate()=0</div><div class="ttdoc">Interface to be implemented by the child class to allocate the tensor.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00790">Validate.h:790</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_activation_layer_xhtml_a0fdcd48f36eb1310d56f0f0d5ce9ab00"><div class="ttname"><a href="classarm__compute_1_1_g_c_activation_layer.xhtml#a0fdcd48f36eb1310d56f0f0d5ce9ab00">arm_compute::GCActivationLayer::configure</a></div><div class="ttdeci">void configure(IGCTensor *input, IGCTensor *output, ActivationLayerInfo act_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_activation_layer_8cpp_source.xhtml#l00037">GCActivationLayer.cpp:37</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_g_c_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::GCTensor::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="_g_c_tensor_8cpp_source.xhtml#l00039">GCTensor.cpp:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">arm_compute::PaddingMode::CONSTANT</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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="classarm__compute_1_1_g_c_tensor_xhtml_a44d1d7d909047fe63f5f6c11a9849986"><div class="ttname"><a href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">arm_compute::GCTensor::allocator</a></div><div class="ttdeci">ITensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_tensor_8cpp_source.xhtml#l00034">GCTensor.cpp:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_col2_im_kernel_xhtml_a1aebc8a9a10fb43c9a39d241d0c11338"><div class="ttname"><a href="classarm__compute_1_1_g_c_col2_im_kernel.xhtml#a1aebc8a9a10fb43c9a39d241d0c11338">arm_compute::GCCol2ImKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, IGCTensor *output, std::pair< unsigned int, unsigned int > convolved_dims)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_col2_im_kernel_8cpp_source.xhtml#l00044">GCCol2ImKernel.cpp:44</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="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">ITensorAllocator::allocate()</a>, <a class="el" href="_g_c_tensor_8cpp_source.xhtml#l00034">GCTensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00790">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_validate_8h_source.xhtml#l00543">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</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#l00152">ARM_COMPUTE_UNUSED</a>, <a class="el" href="_g_c_fill_border_kernel_8cpp_source.xhtml#l00060">GCFillBorderKernel::configure()</a>, <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml#l00044">GCConvolutionLayerReshapeWeights::configure()</a>, <a class="el" href="_g_c_activation_layer_8cpp_source.xhtml#l00037">GCActivationLayer::configure()</a>, <a class="el" href="_g_c_col2_im_kernel_8cpp_source.xhtml#l00044">GCCol2ImKernel::configure()</a>, <a class="el" href="_g_c_im2_col_kernel_8cpp_source.xhtml#l00067">GCIm2ColKernel::configure()</a>, <a class="el" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">arm_compute::CONSTANT</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</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="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00070">GCScheduler::get()</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="_g_c_tensor_8cpp_source.xhtml#l00039">GCTensor::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="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00253">TensorInfo::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="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="_i_g_c_kernel_8h_source.xhtml#l00113">IGCKernel::set_target()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00261">TensorInfo::tensor_shape()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, and <a class="el" href="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">arm_compute::test::validation::weights_info</a>.</p> |
| |
| </div> |
| </div> |
| <a id="a87ed9138ded739663871c103fc77b36a"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a87ed9138ded739663871c103fc77b36a">◆ </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_g_c_convolution_layer.xhtml">GCConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</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">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="a0a2e80b84845bf2538dd5770089913b7"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a0a2e80b84845bf2538dd5770089913b7">◆ </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_g_c_convolution_layer.xhtml">GCConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml">GCConvolutionLayer</a> && </td> |
| <td class="paramname"></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 assignment operator. </p> |
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| </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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| <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="_g_c_convolution_layer_8cpp_source.xhtml#l00226">226</a> of file <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml">GCConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">if</span>(!_is_prepared)</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>  <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="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="comment">// Run weights reshaping and mark as unused</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  _weights_reshaped.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  _reshape_weights.<a class="code" href="classarm__compute_1_1_g_c_convolution_layer_reshape_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// Mark original weights tensor as unused</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</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="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> }</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_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_g_c_convolution_layer_reshape_weights_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_g_c_convolution_layer_reshape_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::GCConvolutionLayerReshapeWeights::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="_g_c_convolution_layer_8cpp_source.xhtml#l00064">GCConvolutionLayer.cpp:64</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_aa8a4946cd749d482dd996874d295af85"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">arm_compute::ITensorAllocator::allocate</a></div><div class="ttdeci">virtual void allocate()=0</div><div class="ttdoc">Interface to be implemented by the child class to allocate the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_tensor_xhtml_a44d1d7d909047fe63f5f6c11a9849986"><div class="ttname"><a href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">arm_compute::GCTensor::allocator</a></div><div class="ttdeci">ITensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_tensor_8cpp_source.xhtml#l00034">GCTensor.cpp:34</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">ITensorAllocator::allocate()</a>, <a class="el" href="_g_c_tensor_8cpp_source.xhtml#l00034">GCTensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00162">ITensor::is_used()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, and <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml#l00064">GCConvolutionLayerReshapeWeights::run()</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml#l00200">GCConvolutionLayer::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"> |
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| <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> |
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| |
| <p>Run the kernels contained in the function. </p> |
| <p>For NEON kernels:</p><ul> |
| <li>Multi-threading is used for the kernels which are parallelisable.</li> |
| <li>By default std::thread::hardware_concurrency() threads are used.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl> |
| <p>For OpenCL kernels:</p><ul> |
| <li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li> |
| <li>The queue is then flushed.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd> |
| <dd> |
| Will call <a class="el" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl> |
| |
| <p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p> |
| |
| <p class="definition">Definition at line <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml#l00200">200</a> of file <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml">GCConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><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>  <a class="code" href="classarm__compute_1_1_g_c_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="comment">// Run im2col</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_fill_border);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_input_im2col_kernel);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="comment">// Run gemm on reshaped matrices</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_g_c_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">// Reshape output matrix</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_output_col2im_kernel, <span class="keyword">false</span>);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// Run Activation Layer</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled)</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>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_i_g_c_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> }</div><div class="ttc" id="classarm__compute_1_1_g_c_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_g_c_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::GCConvolutionLayer::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="_g_c_convolution_layer_8cpp_source.xhtml#l00226">GCConvolutionLayer.cpp:226</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a66a29e27a51a13250143981b0ee4ad19"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">arm_compute::GCScheduler::dispatch</a></div><div class="ttdeci">void dispatch(IGCKernel &kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00077">GCScheduler.cpp:77</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_g_c_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::GCGEMM::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="_g_c_g_e_m_m_8cpp_source.xhtml#l00161">GCGEMM.cpp:161</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_g_c_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_g_c_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::IGCSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_g_c_simple_function_8cpp_source.xhtml#l00038">IGCSimpleFunction.cpp:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a2dcf87458fcfdfb5e9fdd369e0320d78"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">arm_compute::GCScheduler::memory_barrier</a></div><div class="ttdeci">void memory_barrier()</div><div class="ttdoc">Defines a barrier ordering memory transactions.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00086">GCScheduler.cpp:86</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a9c5f715748222ab9607cc52134b36b0b"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">arm_compute::GCScheduler::get</a></div><div class="ttdeci">static GCScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00070">GCScheduler.cpp:70</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00082">IMemoryGroup.h:82</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00077">GCScheduler::dispatch()</a>, <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00070">GCScheduler::get()</a>, <a class="el" href="_g_c_scheduler_8cpp_source.xhtml#l00086">GCScheduler::memory_barrier()</a>, <a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml#l00226">GCConvolutionLayer::prepare()</a>, <a class="el" href="_i_g_c_simple_function_8cpp_source.xhtml#l00038">IGCSimpleFunction::run()</a>, and <a class="el" href="_g_c_g_e_m_m_8cpp_source.xhtml#l00161">GCGEMM::run()</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following files:<ul> |
| <li>arm_compute/runtime/GLES_COMPUTE/functions/<a class="el" href="_g_c_convolution_layer_8h_source.xhtml">GCConvolutionLayer.h</a></li> |
| <li>src/runtime/GLES_COMPUTE/functions/<a class="el" href="_g_c_convolution_layer_8cpp_source.xhtml">GCConvolutionLayer.cpp</a></li> |
| </ul> |
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