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| <div class="title">CLWinogradConvolutionLayer Class Reference</div> </div> |
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| |
| <p>Basic function to execute Winograd-based convolution on OpenCL. |
| <a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_c_l_winograd_convolution_layer_8h_source.xhtml">CLWinogradConvolutionLayer.h</a>></code></p> |
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| Collaboration diagram for CLWinogradConvolutionLayer:</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:a331149c5bce4eb9bf1702b3dffd11da9"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a331149c5bce4eb9bf1702b3dffd11da9">CLWinogradConvolutionLayer</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:a331149c5bce4eb9bf1702b3dffd11da9"><td class="mdescLeft"> </td><td class="mdescRight">Default constructor. <a href="#a331149c5bce4eb9bf1702b3dffd11da9">More...</a><br /></td></tr> |
| <tr class="separator:a331149c5bce4eb9bf1702b3dffd11da9"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a69b1eb94188ee004bb863fd7d2c93e16"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a69b1eb94188ee004bb863fd7d2c93e16">CLWinogradConvolutionLayer</a> (const <a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a69b1eb94188ee004bb863fd7d2c93e16"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a69b1eb94188ee004bb863fd7d2c93e16">More...</a><br /></td></tr> |
| <tr class="separator:a69b1eb94188ee004bb863fd7d2c93e16"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a7c84a0eeee3562d3e3c90858c9c0028f"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a7c84a0eeee3562d3e3c90858c9c0028f">CLWinogradConvolutionLayer</a> (<a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a7c84a0eeee3562d3e3c90858c9c0028f"><td class="mdescLeft"> </td><td class="mdescRight">Default move constructor. <a href="#a7c84a0eeee3562d3e3c90858c9c0028f">More...</a><br /></td></tr> |
| <tr class="separator:a7c84a0eeee3562d3e3c90858c9c0028f"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a548924ae4a250d1010c286069c55d921"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a548924ae4a250d1010c286069c55d921">operator=</a> (const <a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a548924ae4a250d1010c286069c55d921"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a548924ae4a250d1010c286069c55d921">More...</a><br /></td></tr> |
| <tr class="separator:a548924ae4a250d1010c286069c55d921"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a750abd85a10f58e3af2d82c62d21a730"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a750abd85a10f58e3af2d82c62d21a730">operator=</a> (<a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a750abd85a10f58e3af2d82c62d21a730"><td class="mdescLeft"> </td><td class="mdescRight">Default move assignment operator. <a href="#a750abd85a10f58e3af2d82c62d21a730">More...</a><br /></td></tr> |
| <tr class="separator:a750abd85a10f58e3af2d82c62d21a730"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a795a5e806f4334092fc9f491fe794998"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a795a5e806f4334092fc9f491fe794998">configure</a> (<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_activation_layer_info.xhtml">ActivationLayerInfo</a> &act_info=<a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(), bool enable_fast_math=false)</td></tr> |
| <tr class="memdesc:a795a5e806f4334092fc9f491fe794998"><td class="mdescLeft"> </td><td class="mdescRight">Set the input and output tensors. <a href="#a795a5e806f4334092fc9f491fe794998">More...</a><br /></td></tr> |
| <tr class="separator:a795a5e806f4334092fc9f491fe794998"><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_winograd_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_winograd_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:a1c5a3dc6ea10d1f68d76064b82b8b5c2"><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_winograd_convolution_layer.xhtml#a1c5a3dc6ea10d1f68d76064b82b8b5c2">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_activation_layer_info.xhtml">ActivationLayerInfo</a> &act_info=<a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(), bool enable_fast_math=false)</td></tr> |
| <tr class="memdesc:a1c5a3dc6ea10d1f68d76064b82b8b5c2"><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_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a>. <a href="#a1c5a3dc6ea10d1f68d76064b82b8b5c2">More...</a><br /></td></tr> |
| <tr class="separator:a1c5a3dc6ea10d1f68d76064b82b8b5c2"><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 execute Winograd-based convolution on OpenCL. </p> |
| <p>This function calls the following OpenCL functions/kernels:</p> |
| <ol type="1"> |
| <li><a class="el" href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml">CLWinogradInputTransform</a></li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml">CLWinogradFilterTransformKernel</a> (only once)</li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml">CLGEMM</a></li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml">CLWinogradOutputTransformKernel</a> </li> |
| </ol> |
| |
| <p class="definition">Definition at line <a class="el" href="_c_l_winograd_convolution_layer_8h_source.xhtml#l00046">46</a> of file <a class="el" href="_c_l_winograd_convolution_layer_8h_source.xhtml">CLWinogradConvolutionLayer.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="a331149c5bce4eb9bf1702b3dffd11da9"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a331149c5bce4eb9bf1702b3dffd11da9">◆ </a></span>CLWinogradConvolutionLayer() <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_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</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="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00092">92</a> of file <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml">CLWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _original_weights(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a69b1eb94188ee004bb863fd7d2c93e16"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a69b1eb94188ee004bb863fd7d2c93e16">◆ </a></span>CLWinogradConvolutionLayer() <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_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</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="a7c84a0eeee3562d3e3c90858c9c0028f"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a7c84a0eeee3562d3e3c90858c9c0028f">◆ </a></span>CLWinogradConvolutionLayer() <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_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</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="a795a5e806f4334092fc9f491fe794998"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a795a5e806f4334092fc9f491fe794998">◆ </a></span>configure()</h2> |
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| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
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| <td class="memname">void configure </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>input</em>, </td> |
| </tr> |
| <tr> |
| <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> |
| <tr> |
| <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> |
| <tr> |
| <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> |
| </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_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">bool </td> |
| <td class="paramname"><em>enable_fast_math</em> = <code>false</code> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
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| <p>Set the input and output tensors. </p> |
| <dl class="section note"><dt>Note</dt><dd>: This function only works with 3x3,3x1,1x3,5x5,5x1,1x5,7x1 and 1x7 kernels along with unit strides for both NCHW and NHWC data layout </dd> |
| <dd> |
| Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true</dd></dl> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: 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: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Contains padding and stride information described in <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">act_info</td><td>(Optional) Activation layer information in case of a fused activation. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">enable_fast_math</td><td>(Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation available which may introduce a drop of accuracy as well. Default is false </td></tr> |
| </table> |
| </dd> |
| </dl> |
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| <p class="definition">Definition at line <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00098">98</a> of file <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml">CLWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><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>  <span class="comment">// Get indices for the width and height</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><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#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><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#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</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="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dims = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(input-><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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width], input-><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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height]);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</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#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[idx_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#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[idx_height]);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile = winograd_output_tile(input_dims, kernel_size, input-><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#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>());</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">if</span>(!enable_fast_math)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>); <span class="comment">//disable winograd for fp16 if fast math is false.</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(check_support_fast_math(output_tile, kernel_size), <span class="stringliteral">"This Winograd configuration requires enable_fast_math=true"</span>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a> = <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a>(output_tile,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  kernel_size,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  input_dims,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  input-><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#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>());</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>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="comment">// Manage intermediate tensors</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input0);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_batched_mm_output);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Do not manage _input1 as it contains the weights</span></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="comment">// Configure input transform</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  _input_transform.<a class="code" href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml#a703fa268e40647a42ebded33750fe74e">configure</a>(input, &_input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Configure filter transform</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  _filter_transform.<a class="code" href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml#add649717b97789f69133093fe9852bb2">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Configure batched matrix multiply</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  _batched_mm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a34e7b882208ff6720bad2e4f2c7565c5">configure</a>(&_input0, &_input1, <span class="keyword">nullptr</span>, &_batched_mm_output, 1.0f, 0.0f, <a class="code" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">true</span> <span class="comment">/* Reshape weights only for the first run*/</span>, 0, <span class="keyword">false</span>, <span class="keyword">false</span>, <a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">GEMMLowpOutputStageInfo</a>(),</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  (input-><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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>)));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// Configure output transform</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  _output_transform.<a class="code" href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml#a9cbb2ce5deb94bb5e67aaf7bb2c71e73">configure</a>(&_batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// Allocate temporary tensors</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  _input0.<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="l00146"></a><span class="lineno"> 146</span>  _batched_mm_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="l00147"></a><span class="lineno"> 147</span> }</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#l00035">CLTensor.cpp:35</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">ConvolutionLayer.cpp:175</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml">arm_compute::WinogradInfo</a></div><div class="ttdoc">Winograd information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l02043">Types.h:2043</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00599">Winograd.cpp:599</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</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="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00330">Winograd.cpp:330</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#l00055">CLTensor.cpp:55</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_filter_transform_kernel_xhtml_add649717b97789f69133093fe9852bb2"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml#add649717b97789f69133093fe9852bb2">arm_compute::CLWinogradFilterTransformKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_filter_transform_kernel_8cpp_source.xhtml#l00102">CLWinogradFilterTransformKernel.cpp:102</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_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</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_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_output_transform_kernel_xhtml_a9cbb2ce5deb94bb5e67aaf7bb2c71e73"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml#a9cbb2ce5deb94bb5e67aaf7bb2c71e73">arm_compute::CLWinogradOutputTransformKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info=ActivationLayerInfo())</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_output_transform_kernel_8cpp_source.xhtml#l00146">CLWinogradOutputTransformKernel.cpp:146</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">arm_compute::GEMMLowpOutputStageInfo</a></div><div class="ttdoc">GEMMLowp output stage info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01845">Types.h:1845</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="_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#l00789">Validate.h:789</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#l00119">CLTensorAllocator.cpp:119</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_a34e7b882208ff6720bad2e4f2c7565c5"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a34e7b882208ff6720bad2e4f2c7565c5">arm_compute::CLGEMM::configure</a></div><div class="ttdeci">void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00470">CLGEMM.cpp:470</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="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</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_g_e_m_m_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_info.xhtml">arm_compute::GEMMInfo</a></div><div class="ttdoc">GEMM information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01880">Types.h:1880</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_input_transform_xhtml_a703fa268e40647a42ebded33750fe74e"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml#a703fa268e40647a42ebded33750fe74e">arm_compute::CLWinogradInputTransform::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_input_transform_8cpp_source.xhtml#l00033">CLWinogradInputTransform.cpp:33</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#l00326">Helpers.inl:326</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#l00252">TensorInfo.h:252</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">arm_compute::test::validation::act_info</a>, <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor::allocator()</a>, <a class="el" href="_validate_8h_source.xhtml#l00789">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_error_8h_source.xhtml#l00328">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_c_l_winograd_input_transform_8cpp_source.xhtml#l00033">CLWinogradInputTransform::configure()</a>, <a class="el" href="_c_l_winograd_filter_transform_kernel_8cpp_source.xhtml#l00102">CLWinogradFilterTransformKernel::configure()</a>, <a class="el" href="_c_l_winograd_output_transform_kernel_8cpp_source.xhtml#l00146">CLWinogradOutputTransformKernel::configure()</a>, <a class="el" href="_c_l_g_e_m_m_8cpp_source.xhtml#l00470">CLGEMM::configure()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00599">arm_compute::test::validation::conv_info</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">ITensorInfo::data_type()</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="_helpers_8inl_source.xhtml#l00326">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor::info()</a>, <a class="el" href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase< TensorType >::manage()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00252">TensorInfo::tensor_shape()</a>, <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">arm_compute::test::validation::weights</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>, and <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00330">arm_compute::test::validation::winograd_info</a>.</p> |
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| </div> |
| </div> |
| <a id="a548924ae4a250d1010c286069c55d921"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a548924ae4a250d1010c286069c55d921">◆ </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_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</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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| <a id="a750abd85a10f58e3af2d82c62d21a730"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a750abd85a10f58e3af2d82c62d21a730">◆ </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_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
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| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
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| <p>Default move assignment operator. </p> |
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| <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"> |
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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_winograd_convolution_layer_8cpp_source.xhtml#l00216">216</a> of file <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml">CLWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><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="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="comment">// Run filter transform and mark original weights as unused</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  _input1.<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="l00222"></a><span class="lineno"> 222</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>(_filter_transform, <span class="keyword">false</span>);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</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="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="comment">// Prepare GEMM and release reshaped weights if marked unused by CLGEMM</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  _batched_mm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">if</span>(!_input1.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>())</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  _input1.<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="l00230"></a><span class="lineno"> 230</span>  }</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>  <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#ad381d1aed28b4b1e1f5a710633934580">queue</a>().finish();</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</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#l00632">CLGEMM.cpp:632</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#l00041">CLScheduler.cpp:41</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="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#l00055">CLTensor.cpp:55</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_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#l00095">CLScheduler.cpp:95</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ad381d1aed28b4b1e1f5a710633934580"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">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_8h_source.xhtml#l00102">CLScheduler.h:102</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#l00119">CLTensorAllocator.cpp:119</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#l00152">CLTensorAllocator.cpp:152</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor::allocator()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler::enqueue()</a>, <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00152">CLTensorAllocator::free()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00041">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_8cpp_source.xhtml#l00632">CLGEMM::prepare()</a>, and <a class="el" href="_c_l_scheduler_8h_source.xhtml#l00102">CLScheduler::queue()</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00200">CLWinogradConvolutionLayer::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> |
| </tr> |
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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_c_l_winograd_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_winograd_convolution_layer_8cpp_source.xhtml#l00200">200</a> of file <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml">CLWinogradConvolutionLayer.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_c_l_winograd_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 input transform</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  _input_transform.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</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>  <span class="comment">// Run batched matrix multiplication</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  _batched_mm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">// Run output transform</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</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>(_output_transform);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</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#l00572">CLGEMM.cpp:572</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#l00041">CLScheduler.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLWinogradConvolutionLayer::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_winograd_convolution_layer_8cpp_source.xhtml#l00216">CLWinogradConvolutionLayer.cpp:216</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#l00037">ICLSimpleFunction.cpp:37</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#l00095">CLScheduler.cpp:95</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#l00046">IMemoryGroup.h:46</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler::enqueue()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler::get()</a>, <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00216">CLWinogradConvolutionLayer::prepare()</a>, <a class="el" href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction::run()</a>, and <a class="el" href="_c_l_g_e_m_m_8cpp_source.xhtml#l00572">CLGEMM::run()</a>.</p> |
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| </div> |
| </div> |
| <a id="a1c5a3dc6ea10d1f68d76064b82b8b5c2"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a1c5a3dc6ea10d1f68d76064b82b8b5c2">◆ </a></span>validate()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
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| <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_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">bool </td> |
| <td class="paramname"><em>enable_fast_math</em> = <code>false</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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| </div><div class="memdoc"> |
| |
| <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_winograd_convolution_layer.xhtml">CLWinogradConvolutionLayer</a>. </p> |
| <dl class="section note"><dt>Note</dt><dd>: This function only works with 3x3,3x1,1x3,5x5,5x1 and 1x5 kernels along with unit strides for both NCHW and NHWC data layout </dd> |
| <dd> |
| Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true</dd></dl> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: 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: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Contains padding and stride information described in <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">act_info</td><td>(Optional) Activation layer information in case of a fused activation. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">enable_fast_math</td><td>(Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation available which may introduce a drop of accuracy as well. Default is false</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_winograd_convolution_layer_8cpp_source.xhtml#l00149">149</a> of file <a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml">CLWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// Get indeces for the width and height</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dims = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width], input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height]);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->tensor_shape()[idx_width], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->tensor_shape()[idx_height]);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile = winograd_output_tile(input_dims, kernel_size, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>());</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() > (kernel_size.x() / 2u)) || (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() > (kernel_size.x() / 2u))), <span class="stringliteral">"Winograd only supports padding up to half kernel size"</span>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() > (kernel_size.y() / 2u)) || (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() > (kernel_size.y() / 2u))), <span class="stringliteral">"Winograd only supports padding up to half kernel size"</span>);</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">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">if</span>(!enable_fast_math)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>); <span class="comment">//disable winograd for fp16 if fast math is false.</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(check_support_fast_math(output_tile, kernel_size), <span class="stringliteral">"This Winograd configuration requires enable_fast_math=true"</span>);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  }</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>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a> = <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a>(output_tile,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  kernel_size,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  input_dims,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>());</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">// Validate input transform</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input0_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">misc::shape_calculator::compute_winograd_input_transform_shape</a>(*input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input0 = input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(input0_shape);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</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_winograd_input_transform.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">CLWinogradInputTransform::validate</a>(input, &input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>));</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// Validate filter transform</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input1_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">misc::shape_calculator::compute_winograd_filter_transform_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input1 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->set_tensor_shape(input1_shape);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</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_winograd_filter_transform_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">CLWinogradFilterTransformKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</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>  <span class="comment">// Validate batched matrix multiply</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> batched_mm_output_shape = input0.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>();</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  batched_mm_output_shape[0] = input1.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[0];</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> batched_mm_output = input0.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">clone</a>()->set_tensor_shape(batched_mm_output_shape);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</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_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">CLGEMM::validate</a>(&input0, &input1, <span class="keyword">nullptr</span>, &batched_mm_output, 1.0f, 0.0f, <a class="code" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">true</span> <span class="comment">/* Reshape weights only for the first run*/</span>, 0, <span class="keyword">false</span>, <span class="keyword">false</span>,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">GEMMLowpOutputStageInfo</a>(), (input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>))));</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>  <span class="comment">// Configure output transform</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</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_winograd_output_transform_kernel.xhtml#a046b6ea49a42b3303fca3785ec0ce04a">CLWinogradOutputTransformKernel::validate</a>(&batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>));</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>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> }</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="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a04249f91ec2964d21a91bb7038821000"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd input transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00631">ShapeCalculator.h:631</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_filter_transform_kernel_xhtml_a6d3d1d4d66f89908455bc5f05e259ce9"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">arm_compute::CLWinogradFilterTransformKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWinogradFilterTransfor...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_filter_transform_kernel_8cpp_source.xhtml#l00133">CLWinogradFilterTransformKernel.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_output_transform_kernel_xhtml_a046b6ea49a42b3303fca3785ec0ce04a"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml#a046b6ea49a42b3303fca3785ec0ce04a">arm_compute::CLWinogradOutputTransformKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info=ActivationLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWinogradOutputTransfor...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_output_transform_kernel_8cpp_source.xhtml#l00222">CLWinogradOutputTransformKernel.cpp:222</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_afbc359bde9be72a6edab175978e56662"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">arm_compute::TensorInfo::clone</a></div><div class="ttdeci">std::unique_ptr< ITensorInfo > clone() const override</div><div class="ttdoc">Provide a clone of the current object of class T.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00306">TensorInfo.cpp:306</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">ConvolutionLayer.cpp:175</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml">arm_compute::WinogradInfo</a></div><div class="ttdoc">Winograd information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l02043">Types.h:2043</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00599">Winograd.cpp:599</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_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#l00193">Error.h:193</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</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#l00791">Validate.h:791</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="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00330">Winograd.cpp:330</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="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_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a86084036bd3851575ef871ad5bf079a7"><div class="ttname"><a href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(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#l00214">Error.h:214</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">arm_compute::GEMMLowpOutputStageInfo</a></div><div class="ttdoc">GEMMLowp output stage info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01845">Types.h:1845</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a25e3751f07d4b2771a05d8d01a7f7620"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd filter transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00608">ShapeCalculator.h:608</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="classarm__compute_1_1_c_l_g_e_m_m_xhtml_a3493ba7d1f2057740ff5931fa00a44ac"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">arm_compute::CLGEMM::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMM.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00525">CLGEMM.cpp:525</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div> |
| <div class="ttc" id="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_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_g_e_m_m_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_info.xhtml">arm_compute::GEMMInfo</a></div><div class="ttdoc">GEMM information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01880">Types.h:1880</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#l00326">Helpers.inl:326</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#l00252">TensorInfo.h:252</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_input_transform_xhtml_a6d3d1d4d66f89908455bc5f05e259ce9"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">arm_compute::CLWinogradInputTransform::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWinogradInputTransform...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_input_transform_8cpp_source.xhtml#l00041">CLWinogradInputTransform.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">arm_compute::test::validation::act_info</a>, <a class="el" href="_validate_8h_source.xhtml#l00791">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_error_8h_source.xhtml#l00214">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>, <a class="el" href="_error_8h_source.xhtml#l00193">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable< T >::clone()</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00306">TensorInfo::clone()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00608">arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00631">arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00599">arm_compute::test::validation::conv_info</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">ITensorInfo::data_type()</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="_helpers_8inl_source.xhtml#l00326">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00252">TensorInfo::tensor_shape()</a>, <a class="el" href="_c_l_winograd_input_transform_8cpp_source.xhtml#l00041">CLWinogradInputTransform::validate()</a>, <a class="el" href="_c_l_winograd_filter_transform_kernel_8cpp_source.xhtml#l00133">CLWinogradFilterTransformKernel::validate()</a>, <a class="el" href="_c_l_winograd_output_transform_kernel_8cpp_source.xhtml#l00222">CLWinogradOutputTransformKernel::validate()</a>, <a class="el" href="_c_l_g_e_m_m_8cpp_source.xhtml#l00525">CLGEMM::validate()</a>, <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">arm_compute::test::validation::weights</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>, and <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00330">arm_compute::test::validation::winograd_info</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_c_l_convolution_layer_8cpp_source.xhtml#l00135">CLConvolutionLayer::get_convolution_method()</a>, and <a class="el" href="_c_l_convolution_layer_8cpp_source.xhtml#l00091">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_winograd_convolution_layer_8h_source.xhtml">CLWinogradConvolutionLayer.h</a></li> |
| <li>src/runtime/CL/functions/<a class="el" href="_c_l_winograd_convolution_layer_8cpp_source.xhtml">CLWinogradConvolutionLayer.cpp</a></li> |
| </ul> |
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