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| <a href="#pub-methods">Public Member Functions</a> | |
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| <div class="title">NEWinogradConvolutionLayer Class Reference</div> </div> |
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| |
| <p>Basic function to simulate a convolution layer. |
| <a href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_n_e_winograd_convolution_layer_8h_source.xhtml">NEWinogradConvolutionLayer.h</a>></code></p> |
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| Collaboration diagram for NEWinogradConvolutionLayer:</div> |
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| <table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> |
| Public Member Functions</h2></td></tr> |
| <tr class="memitem:a83d886e7456d6a5d67ca145efd4c1aff"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a83d886e7456d6a5d67ca145efd4c1aff">NEWinogradConvolutionLayer</a> (const 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:a83d886e7456d6a5d67ca145efd4c1aff"><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#a83d886e7456d6a5d67ca145efd4c1aff">More...</a><br /></td></tr> |
| <tr class="separator:a83d886e7456d6a5d67ca145efd4c1aff"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a49b542b1a17cd73034736acfa562a8ec"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a49b542b1a17cd73034736acfa562a8ec">configure</a> (const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &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:a49b542b1a17cd73034736acfa562a8ec"><td class="mdescLeft"> </td><td class="mdescRight">Set the input and output tensors. <a href="#a49b542b1a17cd73034736acfa562a8ec">More...</a><br /></td></tr> |
| <tr class="separator:a49b542b1a17cd73034736acfa562a8ec"><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_n_e_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_n_e_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="memitem:a5adf00d4ee113decca5848feb9684e08"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a5adf00d4ee113decca5848feb9684e08">NEWinogradConvolutionLayer</a> (const <a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a5adf00d4ee113decca5848feb9684e08"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a5adf00d4ee113decca5848feb9684e08">More...</a><br /></td></tr> |
| <tr class="separator:a5adf00d4ee113decca5848feb9684e08"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:af00f525fea55946a514cfec3e4b95cf2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#af00f525fea55946a514cfec3e4b95cf2">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:af00f525fea55946a514cfec3e4b95cf2"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#af00f525fea55946a514cfec3e4b95cf2">More...</a><br /></td></tr> |
| <tr class="separator:af00f525fea55946a514cfec3e4b95cf2"><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_n_e_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_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</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 simulate a convolution layer. </p> |
| <p>This function calls the following NEON kernels:</p><ol type="1"> |
| <li><a class="el" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml">NEWinogradLayerTransformWeightsKernel</a> (executed only once in the first call to the <a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb" title="Run the kernels contained in the function.">run()</a> method )</li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml">NEWinogradLayerTransformInputKernel</a></li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a></li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_assembly_dispatch.xhtml">NEGEMMAssemblyDispatch</a></li> |
| <li><a class="el" href="classarm__compute_1_1_c_p_p_permute.xhtml">CPPPermute</a> (three times: weights, input and output)</li> |
| </ol> |
| <dl class="section note"><dt>Note</dt><dd>Some Winograd configurations (i.e. F(2x2, 5x5), F(4x4, 5x5)) are supported only with enable_fast_math = true </dd></dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_winograd_convolution_layer_8h_source.xhtml#l00052">52</a> of file <a class="el" href="_n_e_winograd_convolution_layer_8h_source.xhtml">NEWinogradConvolutionLayer.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="a83d886e7456d6a5d67ca145efd4c1aff"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a83d886e7456d6a5d67ca145efd4c1aff">◆ </a></span>NEWinogradConvolutionLayer() <span class="overload">[1/2]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const 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>Constructor. </p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00263">263</a> of file <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml">NEWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  : _memory_group(memory_manager), _gemm_function(memory_manager), _transform_input_kernel(<span class="keyword">nullptr</span>), _transform_output_kernel(<span class="keyword">nullptr</span>), _transform_weights_kernel(<span class="keyword">nullptr</span>), _activationlayer_function(),</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  _permute_input(), _permute_weights(), _permute_output(), _input_transformed(), _output_transformed(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(),</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  _weights_hwio(), _input(), _weights(), _output(), _is_prepared(<span class="keyword">false</span>), _is_activationlayer_enabled(<span class="keyword">false</span>)</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a5adf00d4ee113decca5848feb9684e08"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a5adf00d4ee113decca5848feb9684e08">◆ </a></span>NEWinogradConvolutionLayer() <span class="overload">[2/2]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</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> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a id="a49b542b1a17cd73034736acfa562a8ec"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a49b542b1a17cd73034736acfa562a8ec">◆ </a></span>configure()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname">void configure </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>input</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>weights</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>biases</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </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"> |
| |
| <p>Set the input and output tensors. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: 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>. Currently only 3x3 and 5x5 kernels are supported. </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>weights</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>. Currently only unit strides are supported. </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> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00270">270</a> of file <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml">NEWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> {</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(validate_arguments(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(), <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>(), (biases != <span class="keyword">nullptr</span>) ? biases->info() : <span class="keyword">nullptr</span>, output->info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// Get indices for the width and height</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout();</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> Size2D input_dims = Size2D(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(width_idx), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(height_idx));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keyword">const</span> Size2D kernel_size = Size2D(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(width_idx), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(height_idx));</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> Size2D output_tile = winograd_output_tile(input_dims, kernel_size);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">if</span>(!enable_fast_math)</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">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="l00292"></a><span class="lineno"> 292</span>  }</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  _weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  _input = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  _output = output;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordtype">int</span> n_gemms = 0;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordtype">int</span> N_BLOCK = 0; <span class="comment">// Size of block used by GEMM.</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">if</span>(kernel_size == Size2D(3, 3))</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  {</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(width_idx) > 4 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(height_idx) > 4)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  }</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  }</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  }</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(5, 5))</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  }</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(1, 3))</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  {</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(3, 1))</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(1, 5))</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  {</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1>;</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  }</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(5, 1))</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  {</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5>;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  }</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(1, 7))</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  {</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1>;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  }</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(7, 1))</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keyword">using</span> config = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  }</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  {</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <a class="code" href="_error_8h.xhtml#a7cf8d8b669b8f7b05680230be30d60f4">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Not supported."</span>);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  }</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keyword">const</span> PaddingType use_padding_type = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> use_same_padding = use_padding_type == PADDING_SAME;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_channels = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(channel_idx);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_channels = output->info()->dimension(channel_idx);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> Tensor4DShape in_shape(internal_get_input_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>));</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type();</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> data_type_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->element_size();</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="comment">// Get the memory required to instantiate a new Winograd operator.</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  constexpr <span class="keywordtype">size_t</span> storage_alignment = 64;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="comment">// Kernel Storage</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  in_channels)</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  * data_type_size;</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="comment">// Input storage</span></div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  use_same_padding)</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  * data_type_size;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="comment">// Output storage</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a> = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_matrix_stride = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="comment">// Configure GEMM</span></div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> tile_rows = <a class="code" href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.first, output_tile.height);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> tile_cols = <a class="code" href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.second, output_tile.width);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> m = in_shape.n_batches * tile_rows * tile_cols;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> k = in_shape.n_channels;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> n = out_channels;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> kernel_matrix_row_stride = <a class="code" href="utils_8hpp.xhtml#a8f6fbf8b243a10af40ce8d47a1013384">roundup</a>(out_channels, N_BLOCK);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_matrix_row_stride = kernel_matrix_row_stride;</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  TensorShape a_shape(k, m, 1, n_gemms);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  Strides a_strides(data_type_size);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  a_strides.set(1, a_strides[0] * k);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="comment">//a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  a_strides.set(2, 0);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  a_strides.set(3, data_type_size * input_matrix_stride);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  TensorShape b_shape(n, k, n_gemms);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  Strides b_strides(data_type_size);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  b_strides.set(1, data_type_size * kernel_matrix_row_stride);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  b_strides.set(2, data_type_size * kernel_matrix_stride);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  TensorShape d_shape(n, m, 1, n_gemms);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  Strides d_strides(data_type_size);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  d_strides.set(1, data_type_size * output_matrix_row_stride);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">//d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  d_strides.set(2, 0);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  d_strides.set(3, data_type_size * output_matrix_stride);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  TensorInfo a_info{};</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  TensorInfo b_info{};</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  TensorInfo d_info{};</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  a_info.init(a_shape, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, a_strides, 0, input_storage_size);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  b_info.init(b_shape, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, b_strides, 0, kernel_storage_size);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  d_info.init(d_shape, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, d_strides, 0, output_storage_size);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  _input_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(a_info, storage_alignment);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  _kernel_storage.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(b_info, storage_alignment);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  _output_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(d_info, storage_alignment);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> </div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="comment">// configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  TensorInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>(TensorShape(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2), _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0),</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1), _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(3)),</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  1, _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  _output_nhwc.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keyword">const</span> ITensor *input_to_use = _input;</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  ITensor *output_to_use = _output;</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a> weights_permutation_vector(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_num_threads = <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">num_threads</a>();</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="comment">// Configure the kernel to transform the input tensor from NCHW -> NHWC</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  {</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_input_nhwc);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  _permute_input.<a class="code" href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &_input_nhwc, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  input_to_use = &_input_nhwc;</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  weights_permutation_vector = <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  }</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="comment">// Configure input transform kernel</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_input_transformed);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_input_workspace);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  &_input_transformed, input_matrix_stride, &_input_workspace);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, _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>());</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  _input_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(input_workspace_info);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  _input_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  {</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  _input_nhwc.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  }</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span> </div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="comment">// Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  _permute_weights.<a class="code" href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_weights_hwio, weights_permutation_vector);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="comment">// Configure GEMM function</span></div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_output_transformed);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  _gemm_function.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">configure</a>(&_input_transformed, &_kernel_storage, <span class="keyword">nullptr</span>, &_output_transformed, 1.0f, 0.f);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  _input_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="comment">// Configure output transform function</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="comment">// The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  {</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_output_nhwc);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  output_to_use = &_output_nhwc;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  }</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_gemm::Activation</a> activation = arm_gemm_activation_from_acl_activation(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  transform_output_kernel->configure(biases,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  &_output_transformed,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  output_matrix_stride,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  output_to_use,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  in_shape.n_batches,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.first,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.second,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  out_channels,</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  &_output_workspace,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  activation);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  _output_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(output_workspace_info);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  _output_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  _output_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="comment">// Reorder the convoluted output to ACL's ordering NCHW</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  {</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  _permute_output.<a class="code" href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(&_output_nhwc, _output, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  _output_nhwc.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  }</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  _transform_input_kernel = std::move(transform_input_kernel);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  _transform_weights_kernel = std::move(transform_weights_kernel);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  _transform_output_kernel = std::move(transform_output_kernel);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">//Configure Activation Layer</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  _is_activationlayer_enabled = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled() && ! fuse_function_supported(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  {</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">configure</a>(_output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  }</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> }</div><div class="ttc" id="utils_8hpp_xhtml_aa4508679e1d089c2bdcf000f72357683"><div class="ttname"><a href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a></div><div class="ttdeci">T iceildiv(const T a, const T b)</div><div class="ttdef"><b>Definition:</b> <a href="utils_8hpp_source.xhtml#l00038">utils.hpp:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">ConvolutionLayer.cpp:183</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a7cf8d8b669b8f7b05680230be30d60f4"><div class="ttname"><a href="_error_8h.xhtml#a7cf8d8b669b8f7b05680230be30d60f4">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(msg)</div><div class="ttdoc">Print the given message then throw an std::runtime_error.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00352">Error.h:352</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00597">Winograd.cpp:597</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div> |
| <div class="ttc" id="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_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00048">Types.h:48</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a5f9016ea3e28a033b7cc216bdda912be"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_compute::graph::Activation</a></div><div class="ttdeci">arm_compute::ActivationLayerInfo::ActivationFunction Activation</div><div class="ttdoc">Constant TensorID specifying an equivalent of null tensor.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00068">Types.h:68</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel.</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a0b0eb3235749a2909dc5a101afe59a1b"><div class="ttname"><a href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00456">Error.h:456</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="utils_8hpp_xhtml_a8f6fbf8b243a10af40ce8d47a1013384"><div class="ttname"><a href="utils_8hpp.xhtml#a8f6fbf8b243a10af40ce8d47a1013384">roundup</a></div><div class="ttdeci">T roundup(const T a, const T b)</div><div class="ttdef"><b>Definition:</b> <a href="utils_8hpp_source.xhtml#l00043">utils.hpp:43</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_a385241dcc5062af6ecac8bdafe01bb2a"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">arm_compute::NEGEMM::configure</a></div><div class="ttdeci">void configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00051">NEGEMM.cpp:51</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_activation_layer_xhtml_adfb5ef37594fc9371c4a2b95e3d5e31b"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">arm_compute::NEActivationLayer::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, ActivationLayerInfo activation_info)</div><div class="ttdoc">[NEActivationLayer snippet]</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_8cpp_source.xhtml#l00036">NEActivationLayer.cpp:36</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ab1806bf0c5a41f674fb9d2dc6af644f5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_p_p_permute_xhtml_a93c836ab36443b23753d99495761daf7"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">arm_compute::CPPPermute::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const PermutationVector &perm)</div><div class="ttdoc">Configure the permute CPP kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_permute_8cpp_source.xhtml#l00031">CPPPermute.cpp:31</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_ac24584a63e484123e3756d1b2a1c9e2f"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">arm_compute::IScheduler::num_threads</a></div><div class="ttdeci">virtual unsigned int num_threads() const =0</div><div class="ttdoc">Returns the number of threads that the SingleThreadScheduler has in his pool.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5f5b6c4337eac9e2e0046ca2304d80dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">ArithmeticAddition.cpp:138</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00075">Types.h:75</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">arm_compute::test::validation::act_info</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00352">ARM_COMPUTE_ERROR</a>, <a class="el" href="_error_8h_source.xhtml#l00456">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00455">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="_c_p_p_permute_8cpp_source.xhtml#l00031">CPPPermute::configure()</a>, <a class="el" href="_n_e_activation_layer_8cpp_source.xhtml#l00036">NEActivationLayer::configure()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00051">NEGEMM::configure()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">arm_compute::test::validation::data_layout</a>, <a class="el" href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">arm_compute::test::validation::data_type</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">ITensorInfo::data_type()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00232">TensorInfo::dimension()</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="utils_8hpp_source.xhtml#l00038">iceildiv()</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor::info()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::info</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator::init()</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00107">TensorInfo::init()</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::NCHW</a>, <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">IScheduler::num_threads()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::output_shape</a>, <a class="el" href="utils_8hpp_source.xhtml#l00043">roundup()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00074">Dimensions< T >::set()</a>, <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::U</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p> |
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| <h2 class="memtitle"><span class="permalink"><a href="#af00f525fea55946a514cfec3e4b95cf2">◆ </a></span>operator=()</h2> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml">NEWinogradConvolutionLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
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| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
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| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
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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> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
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| <p>Prepare the function for executing. </p> |
| <p>Any one off pre-processing step required by the function is handled here</p> |
| <dl class="section note"><dt>Note</dt><dd>Prepare stage might not need all the function's buffers' backing memory to be available in order to execute </dd></dl> |
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| <p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p> |
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| <p class="definition">Definition at line <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00695">695</a> of file <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml">NEWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  {</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="comment">// Permute weights</span></div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  _weights_hwio.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  _permute_weights.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  _weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="comment">// Transform weights</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  _kernel_storage.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_transform_weights_kernel.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span> </div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  _weights_hwio.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  }</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> }</div><div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::TensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00148">TensorAllocator.cpp:148</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_p_p_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICPPSimpleFunction::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_p_p_simple_function_8cpp_source.xhtml#l00035">ICPPSimpleFunction.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00148">TensorAllocator::free()</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, <a class="el" href="_i_c_p_p_simple_function_8cpp_source.xhtml#l00035">ICPPSimpleFunction::run()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00552">NEWinogradConvolutionLayer::run()</a>.</p> |
| |
| </div> |
| </div> |
| <a id="ad1717410afd0be936c6213a63c8005fb"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">◆ </a></span>run()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname">void run </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
| </tr> |
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| </div><div class="memdoc"> |
| |
| <p>Run the kernels contained in the function. </p> |
| <p>For NEON kernels:</p><ul> |
| <li>Multi-threading is used for the kernels which are parallelisable.</li> |
| <li>By default std::thread::hardware_concurrency() threads are used.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl> |
| <p>For OpenCL kernels:</p><ul> |
| <li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li> |
| <li>The queue is then flushed.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd> |
| <dd> |
| Will call <a class="el" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl> |
| |
| <p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00552">552</a> of file <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml">NEWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> {</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = _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="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  {</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="comment">//Bring channels to the front as Winograd code expects the tensor to be in the format NHWC</span></div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  _permute_input.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  }</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="comment">// Transform input tensor to the winograd domain</span></div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_transform_input_kernel.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> </div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="comment">//Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs</span></div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  _gemm_function.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="comment">// Transform output tensor to the spatial domain</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_transform_output_kernel.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  {</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="comment">// Reorder the convoluted output to ACL's ordering NCHW</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  _permute_output.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  }</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled )</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  {</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  }</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMM::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM.cpp:285</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEWinogradConvolutionLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00695">NEWinogradConvolutionLayer.cpp:695</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_p_p_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICPPSimpleFunction::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_p_p_simple_function_8cpp_source.xhtml#l00035">ICPPSimpleFunction.cpp:35</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">arm_compute::test::validation::data_layout</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::NCHW</a>, <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00695">NEWinogradConvolutionLayer::prepare()</a>, <a class="el" href="_i_c_p_p_simple_function_8cpp_source.xhtml#l00035">ICPPSimpleFunction::run()</a>, <a class="el" href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder::run()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM::run()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p> |
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| <a id="a1c5a3dc6ea10d1f68d76064b82b8b5c2"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a1c5a3dc6ea10d1f68d76064b82b8b5c2">◆ </a></span>validate()</h2> |
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| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
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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> |
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| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">static</span></span> </td> |
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| <p>Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a>. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: 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>. Currently only 3x3 and 5x5 kernels are supported. </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>weights</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">output</td><td>Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Contains padding and stride information described in <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>. Currently only unit strides are supported. </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="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00587">587</a> of file <a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml">NEWinogradConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> {</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_arguments(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>));</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="comment">// Get indices for the width and height</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> </div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keyword">const</span> Size2D input_dims = Size2D(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_width), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_height));</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keyword">const</span> Size2D kernel_size = Size2D(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_width), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_height));</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="keyword">const</span> Size2D output_tile = winograd_output_tile(input_dims, kernel_size);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="comment">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="keywordflow">if</span>(!enable_fast_math)</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  {</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">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="l00606"></a><span class="lineno"> 606</span>  }</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span> </div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keyword">const</span> WinogradInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a> = WinogradInfo(output_tile,</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  kernel_size,</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  input_dims,</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout());</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="comment">// Validate input transform</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keyword">const</span> TensorShape input0_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">misc::shape_calculator::compute_winograd_input_transform_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keyword">const</span> TensorInfo input0 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->clone()->set_tensor_shape(input0_shape);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="comment">// Validate filter transform</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keyword">const</span> TensorShape 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="l00619"></a><span class="lineno"> 619</span>  <span class="keyword">const</span> TensorInfo 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="l00620"></a><span class="lineno"> 620</span>  <span class="comment">// Validate batched matrix multiply</span></div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  TensorShape batched_mm_output_shape = input0.tensor_shape();</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  batched_mm_output_shape[0] = input1.tensor_shape()[0];</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="keyword">const</span> TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keywordflow">if</span>(kernel_size == Size2D(3, 3))</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  {</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordflow">return</span> validate_kernel_3x3(input_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00635"></a><span class="lineno"> 635</span>  }</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(5, 5))</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  {</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keywordflow">return</span> validate_kernel_5x5(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00646"></a><span class="lineno"> 646</span>  }</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">if</span>(kernel_size == Size2D(3, 1))</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="keywordflow">return</span> validate_kernel_3x1(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00653"></a><span class="lineno"> 653</span>  }</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(1, 3))</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  {</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">return</span> validate_kernel_1x3(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00660"></a><span class="lineno"> 660</span>  }</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(5, 1))</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  {</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keywordflow">return</span> validate_kernel_5x1(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00667"></a><span class="lineno"> 667</span>  }</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(1, 5))</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  {</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordflow">return</span> validate_kernel_1x5(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00674"></a><span class="lineno"> 674</span>  }</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(7, 1))</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  {</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keywordflow">return</span> validate_kernel_7x1(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00681"></a><span class="lineno"> 681</span>  }</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == Size2D(1, 7))</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  {</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keywordflow">return</span> validate_kernel_1x7(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#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="l00688"></a><span class="lineno"> 688</span>  }</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  {</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="_error_8h.xhtml#a9561091f3566e78ad3aa39259bc4126a">ARM_COMPUTE_RETURN_ERROR_MSG</a>(<span class="stringliteral">"Kernel shape not supported"</span>);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  }</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> }</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#l00667">ShapeCalculator.h:667</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">ConvolutionLayer.cpp:183</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00597">Winograd.cpp:597</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00204">Error.h:204</a></div></div> |
| <div class="ttc" id="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#l00328">Winograd.cpp:328</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_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#l00644">ShapeCalculator.h:644</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a9561091f3566e78ad3aa39259bc4126a"><div class="ttname"><a href="_error_8h.xhtml#a9561091f3566e78ad3aa39259bc4126a">ARM_COMPUTE_RETURN_ERROR_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_MSG(...)</div><div class="ttdoc">An error is returned with the given description.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00194">Error.h:194</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a1c69762a42ab8add645d0a949b6f4b1f"><div class="ttname"><a href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">arm_compute::test::validation::act_info</a>, <a class="el" href="_error_8h_source.xhtml#l00194">ARM_COMPUTE_RETURN_ERROR_MSG</a>, <a class="el" href="_error_8h_source.xhtml#l00244">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>, <a class="el" href="_validate_8h_source.xhtml#l00163">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00314">TensorInfo::clone()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00644">arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00667">arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00261">TensorInfo::tensor_shape()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>, and <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00328">arm_compute::test::validation::winograd_info</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_n_e_convolution_layer_8cpp_source.xhtml#l00120">NEConvolutionLayer::get_convolution_method()</a>, and <a class="el" href="_n_e_convolution_layer_8cpp_source.xhtml#l00089">NEConvolutionLayer::validate()</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following files:<ul> |
| <li>arm_compute/runtime/NEON/functions/<a class="el" href="_n_e_winograd_convolution_layer_8h_source.xhtml">NEWinogradConvolutionLayer.h</a></li> |
| <li>src/runtime/NEON/functions/<a class="el" href="_n_e_winograd_convolution_layer_8cpp_source.xhtml">NEWinogradConvolutionLayer.cpp</a></li> |
| </ul> |
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