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| author | mathad01 <matthew.haddon@arm.com> | 2021-05-10 16:51:20 +0100 |
|---|---|---|
| committer | matthew.haddon <matthew.haddon@arm.com> | 2021-05-12 07:59:04 +0000 |
| commit | add078b7f9a658d52ee6e8fe6771ea0517c07674 (patch) | |
| tree | 104b4aac324234c13f83372bec194385ca45b1db /21.05/_fully_connected_test_impl_8hpp.xhtml | |
| parent | a24952b12a56da56b6acabfd07cc60b1663720f9 (diff) | |
| download | armnn-add078b7f9a658d52ee6e8fe6771ea0517c07674.tar.gz | |
IVGCVSW-5908 Update 21.05 Doxygen Documents
Signed-off-by: mathad01 <matthew.haddon@arm.com>
Change-Id: I95316d4fc5f9d10185492dc835bb2411c1daea7b
Diffstat (limited to '21.05/_fully_connected_test_impl_8hpp.xhtml')
| -rw-r--r-- | 21.05/_fully_connected_test_impl_8hpp.xhtml | 285 |
1 files changed, 285 insertions, 0 deletions
diff --git a/21.05/_fully_connected_test_impl_8hpp.xhtml b/21.05/_fully_connected_test_impl_8hpp.xhtml new file mode 100644 index 0000000000..86d2984c30 --- /dev/null +++ b/21.05/_fully_connected_test_impl_8hpp.xhtml @@ -0,0 +1,285 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.hpp File Reference</title> +<link href="tabs.css" rel="stylesheet" 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top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_fully_connected_test_impl_8hpp.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="summary"> +<a href="#func-members">Functions</a> </div> + <div class="headertitle"> +<div class="title">FullyConnectedTestImpl.hpp File Reference</div> </div> +</div><!--header--> +<div class="contents"> +<div class="textblock"><code>#include "<a class="el" href="_layer_test_result_8hpp_source.xhtml">LayerTestResult.hpp</a>"</code><br /> +<code>#include <<a class="el" href="_resolve_type_8hpp_source.xhtml">ResolveType.hpp</a>></code><br /> +<code>#include <<a class="el" href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml">armnn/backends/IBackendInternal.hpp</a>></code><br /> +<code>#include <<a class="el" href="_workload_factory_8hpp_source.xhtml">backendsCommon/WorkloadFactory.hpp</a>></code><br /> +</div> +<p><a href="_fully_connected_test_impl_8hpp_source.xhtml">Go to the source code of this file.</a></p> +<table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a> +Functions</h2></td></tr> +<tr class="memitem:a59a2fc917d2bd6687858f4ace9617a97"><td class="memTemplParams" colspan="2">template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> </td></tr> +<tr class="memitem:a59a2fc917d2bd6687858f4ace9617a97"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>< T, 2 > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_fully_connected_test_impl_8hpp.xhtml#a59a2fc917d2bd6687858f4ace9617a97">FullyConnectedTest</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &memoryManager, const <a class="el" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a> &tensorHandleFactory, bool biasEnabled, bool constantWeights)</td></tr> +<tr class="separator:a59a2fc917d2bd6687858f4ace9617a97"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:afb505feb224a201164ab815b8a6159cf"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>< float, 2 > </td><td class="memItemRight" valign="bottom"><a class="el" href="_fully_connected_test_impl_8hpp.xhtml#afb505feb224a201164ab815b8a6159cf">FullyConnectedFloat32Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &memoryManager, const <a class="el" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a> &tensorHandleFactory, bool biasEnabled, bool transposeWeights)</td></tr> +<tr class="separator:afb505feb224a201164ab815b8a6159cf"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:aa50f043364dd453338261e82397c3e1b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>< float, 2 > </td><td class="memItemRight" valign="bottom"><a class="el" href="_fully_connected_test_impl_8hpp.xhtml#aa50f043364dd453338261e82397c3e1b">FullyConnectedLargeTest</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &memoryManager, const <a class="el" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a> &tensorHandleFactory, bool transposeWeights)</td></tr> +<tr class="separator:aa50f043364dd453338261e82397c3e1b"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<h2 class="groupheader">Function Documentation</h2> +<a id="afb505feb224a201164ab815b8a6159cf"></a> +<h2 class="memtitle"><span class="permalink"><a href="#afb505feb224a201164ab815b8a6159cf">◆ </a></span>FullyConnectedFloat32Test()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><float, 2> FullyConnectedFloat32Test </td> + <td>(</td> + <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> & </td> + <td class="paramname"><em>workloadFactory</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> & </td> + <td class="paramname"><em>memoryManager</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a> & </td> + <td class="paramname"><em>tensorHandleFactory</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">bool </td> + <td class="paramname"><em>biasEnabled</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">bool </td> + <td class="paramname"><em>transposeWeights</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_fully_connected_test_impl_8cpp_source.xhtml#l00334">334</a> of file <a class="el" href="_fully_connected_test_impl_8cpp_source.xhtml">FullyConnectedTestImpl.cpp</a>.</p> + +<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00042">LayerTestResult< T, n >::outputExpected</a>, and <a class="el" href="_descriptors_8cpp_source.xhtml#l00350">armnn::swap()</a>.</p> +<div class="fragment"><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> {</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels };</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { inputChannels, outputChannels };</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="namespacearmnn.xhtml#a14d7f180bf51e86850305965c3707e07">std::swap</a>(weightsShape[0], weightsShape[1]);</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> </div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { outputChannels };</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  weightsDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(2, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  biasesDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> result(outputTensorInfo);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  boost::multi_array<float, 4> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  })</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  );</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  boost::multi_array<float, 2> weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(</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>  .5f, 2.f, .5f,</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  .5f, 2.f, 1.f,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  .5f, 2.f, 2.f,</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  .5f, 2.f, 3.f,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  .5f, 2.f, 4.f</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  }));</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">if</span> (transposeWeights)</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>  weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(</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>  .5f, .5f, .5f, .5f, .5f,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  2.f, 2.f, 2.f, 2.f, 2.f,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  .5f, 1.f, 2.f, 3.f, 4.f</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>  }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  std::vector<float> biasValues({0.f, 0.f, 0.f});</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  {</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  biasValues = std::vector<float>({10.f, 20.f, 30.f});</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  }</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  boost::multi_array<float, 1> bias = MakeTensor<float, 1>(biasesDesc, biasValues);</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>  result = SimpleFullyConnectedTestImpl<float>(</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  workloadFactory,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  memoryManager,</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  tensorHandleFactory,</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  weightsDesc, biasesDesc,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  weights, bias, input,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  biasEnabled, transposeWeights</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  );</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, std::vector<float>(</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  {</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2]</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  })</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  );</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a14d7f180bf51e86850305965c3707e07"><div class="ttname"><a href="namespacearmnn.xhtml#a14d7f180bf51e86850305965c3707e07">armnn::swap</a></div><div class="ttdeci">void swap(OriginsDescriptor &first, OriginsDescriptor &second)</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00350">Descriptors.cpp:350</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00030">LayerTestResult.hpp:30</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="aa50f043364dd453338261e82397c3e1b"></a> +<h2 class="memtitle"><span class="permalink"><a href="#aa50f043364dd453338261e82397c3e1b">◆ </a></span>FullyConnectedLargeTest()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><float, 2> FullyConnectedLargeTest </td> + <td>(</td> + <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> & </td> + <td class="paramname"><em>workloadFactory</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> & </td> + <td class="paramname"><em>memoryManager</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a> & </td> + <td class="paramname"><em>tensorHandleFactory</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">bool </td> + <td class="paramname"><em>transposeWeights</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_fully_connected_test_impl_8cpp_source.xhtml#l00433">433</a> of file <a class="el" href="_fully_connected_test_impl_8cpp_source.xhtml">FullyConnectedTestImpl.cpp</a>.</p> +<div class="fragment"><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> {</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keywordflow">return</span> FullyConnectedLargeTestCommon<armnn::DataType::Float32>(workloadFactory,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  memoryManager,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  tensorHandleFactory,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  transposeWeights);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> }</div></div><!-- fragment --> +</div> +</div> +<a id="a59a2fc917d2bd6687858f4ace9617a97"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a59a2fc917d2bd6687858f4ace9617a97">◆ </a></span>FullyConnectedTest()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><T, 2> FullyConnectedTest </td> + <td>(</td> + <td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> & </td> + <td class="paramname"><em>workloadFactory</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> & </td> + <td class="paramname"><em>memoryManager</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a> & </td> + <td class="paramname"><em>tensorHandleFactory</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">bool </td> + <td class="paramname"><em>biasEnabled</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">bool </td> + <td class="paramname"><em>constantWeights</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_fully_connected_test_impl_8cpp_source.xhtml#l00128">128</a> of file <a class="el" href="_fully_connected_test_impl_8cpp_source.xhtml">FullyConnectedTestImpl.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_layer_support_rules_8hpp_source.xhtml#l00014">armnn::GetBiasTypeFromWeightsType()</a>, <a class="el" href="_layer_test_result_8hpp_source.xhtml#l00042">LayerTestResult< T, n >::outputExpected</a>, and <a class="el" href="_tensor_8cpp_source.xhtml#l00464">TensorInfo::SetQuantizationScale()</a>.</p> + +<p class="reference">Referenced by <a class="el" href="_fuse_activation_tests_8cpp_source.xhtml#l00026">armnn::GetVector()</a>.</p> +<div class="fragment"><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3u;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2u;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputWidth * inputHeight * inputChannels;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 2u;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.1f);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  inputTensorInfo.SetQuantizationOffset(63);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, outputChannels }, ArmnnType);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(5.f);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc({ outputChannels, inputSize }, ArmnnType);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  weightsDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.2f);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  weightsDesc.SetQuantizationOffset(93);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc({ outputChannels }, <a class="code" href="namespacearmnn.xhtml#a83c4a275acf59f62b8387f389d0929d5">GetBiasTypeFromWeightsType</a>(weightsDesc.GetDataType()).value());</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  biasesDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale());</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  biasesDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result(outputTensorInfo);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>(</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  -1.2f, 6.1f, -3.5f,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  18.8f, -5.5f, 2.9f</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  },</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  inputTensorInfo));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">auto</span> weights = MakeTensor<T, 2>(weightsDesc, ConvertToDataType<ArmnnType>(</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  },</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  weightsDesc));</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">auto</span> bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500});</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordflow">if</span> (constantWeights)</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  result = SimpleFullyConnectedTestImpl<T>(workloadFactory,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  memoryManager,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  tensorHandleFactory,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  inputTensorInfo,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  outputTensorInfo,</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  weightsDesc,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  biasesDesc,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  weights,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  bias,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  input,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  biasEnabled,</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  result = SimpleFullyConnectedTestWeightsAsInputsImpl<T>(workloadFactory,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  memoryManager,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  tensorHandleFactory,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  inputTensorInfo,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  outputTensorInfo,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  weightsDesc,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  biasesDesc,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  weights,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  bias,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  input,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  biasEnabled,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  }</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  result.outputExpected = MakeTensor<T, 2>(outputTensorInfo,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  ConvertToDataType<ArmnnType>({80.f, 1460.f}, outputTensorInfo));</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  result.outputExpected = MakeTensor<T, 2>(outputTensorInfo,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  ConvertToDataType<ArmnnType>({-107.04f, 110.f}, outputTensorInfo));</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  }</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a83c4a275acf59f62b8387f389d0929d5"><div class="ttname"><a href="namespacearmnn.xhtml#a83c4a275acf59f62b8387f389d0929d5">armnn::GetBiasTypeFromWeightsType</a></div><div class="ttdeci">armnn::Optional< armnn::DataType > GetBiasTypeFromWeightsType(armnn::Optional< armnn::DataType > weightsType)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_rules_8hpp_source.xhtml#l00014">LayerSupportRules.hpp:14</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00464">Tensor.cpp:464</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00030">LayerTestResult.hpp:30</a></div></div> +</div><!-- fragment --> +</div> +</div> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for 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