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| <div class="textblock"><code>#include "<a class="el" href="_batch_normalization_test_impl_8hpp_source.xhtml">BatchNormalizationTestImpl.hpp</a>"</code><br /> |
| <code>#include <<a class="el" href="_quantize_helper_8hpp_source.xhtml">QuantizeHelper.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="_ignore_unused_8hpp_source.xhtml">armnn/utility/IgnoreUnused.hpp</a>></code><br /> |
| <code>#include <<a class="el" href="_data_layout_indexed_8hpp_source.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></code><br /> |
| <code>#include <<a class="el" href="_cpu_tensor_handle_8hpp_source.xhtml">backendsCommon/CpuTensorHandle.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 /> |
| <code>#include <<a class="el" href="_tensor_copy_utils_8hpp_source.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>></code><br /> |
| <code>#include <<a class="el" href="_workload_test_utils_8hpp_source.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>></code><br /> |
| <code>#include <<a class="el" href="_tensor_helpers_8hpp_source.xhtml">test/TensorHelpers.hpp</a>></code><br /> |
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| <p><a href="_batch_normalization_test_impl_8cpp_source.xhtml">Go to the source code of this file.</a></p> |
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| Functions</h2></td></tr> |
| <tr class="memitem:a95e3411d80e0eac3844844c017f03861"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>< float, 4 > </td><td class="memItemRight" valign="bottom"><a class="el" href="_batch_normalization_test_impl_8cpp.xhtml#a95e3411d80e0eac3844844c017f03861">BatchNormFloat32Test</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)</td></tr> |
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| <tr class="memitem:a0fe6b55e33196820f9bf4759647c17df"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>< <a class="el" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">armnn::Half</a>, 4 > </td><td class="memItemRight" valign="bottom"><a class="el" href="_batch_normalization_test_impl_8cpp.xhtml#a0fe6b55e33196820f9bf4759647c17df">BatchNormFloat16Test</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)</td></tr> |
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| <tr class="memitem:a7615443ac0887d4c282f53f7e49d889c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>< <a class="el" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">armnn::Half</a>, 4 > </td><td class="memItemRight" valign="bottom"><a class="el" href="_batch_normalization_test_impl_8cpp.xhtml#a7615443ac0887d4c282f53f7e49d889c">BatchNormFloat16NhwcTest</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)</td></tr> |
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| <tr class="memitem:a39988d3dc5c636fa49e8192f26d72554"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a>< float, 4 > </td><td class="memItemRight" valign="bottom"><a class="el" href="_batch_normalization_test_impl_8cpp.xhtml#a39988d3dc5c636fa49e8192f26d72554">CompareBatchNormTest</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, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &refWorkloadFactory)</td></tr> |
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| <a id="a7615443ac0887d4c282f53f7e49d889c"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a7615443ac0887d4c282f53f7e49d889c">◆ </a></span>BatchNormFloat16NhwcTest()</h2> |
| |
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| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><<a class="el" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">armnn::Half</a>, 4> BatchNormFloat16NhwcTest </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></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
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| </div><div class="memdoc"> |
| |
| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00349">349</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
| |
| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> {</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  std::vector<float> inputValues</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>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  1.f, 1.f,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  4.f, 1.f,</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="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  4.f, 4.f,</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  2.f, 1.f,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  1.f, -2.f,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  6.f, 4.f</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>  std::vector<float> expectedOutputValues</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  1.f, 3.f,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  4.f, 3.f,</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>  <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  4.f, 4.f,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  2.f, 3.f,</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="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  1.f, 2.f,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  6.f, 4.f</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  };</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::Float16>(</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  workloadFactory,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  memoryManager,</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  inputOutputShape,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  inputValues,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  expectedOutputValues,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  0.f,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  0,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> |
| </div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a0fe6b55e33196820f9bf4759647c17df"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a0fe6b55e33196820f9bf4759647c17df">◆ </a></span>BatchNormFloat16Test()</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><<a class="el" href="namespacearmnn.xhtml#a0f38fa92b2468d5378258a2b074c1a31">armnn::Half</a>, 4> BatchNormFloat16Test </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></td> |
| <td>)</td> |
| <td></td><td></td> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00303">303</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
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| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> {</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  std::vector<float> inputValues</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  1.f, 4.f,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  4.f, 2.f,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  1.f, 6.f,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  1.f, 1.f,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  4.f, 1.f,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  -2.f, 4.f</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  };</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  std::vector<float> expectedOutputValues</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="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  1.f, 4.f,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  4.f, 2.f,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  1.f, 6.f,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  3.f, 3.f,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  4.f, 3.f,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  2.f, 4.f</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  };</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="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::Float16>(</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  workloadFactory,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  memoryManager,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  inputOutputShape,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  inputValues,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  expectedOutputValues,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  0.f,</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  0,</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div> |
| </div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a449a360cd864483064ae2991db8edcd8"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a449a360cd864483064ae2991db8edcd8">◆ </a></span>BatchNormFloat32NhwcTest()</h2> |
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| <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, 4> BatchNormFloat32NhwcTest </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></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00253">253</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
| |
| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> {</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  std::vector<float> inputValues</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  1.f, 1.f,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  4.f, 1.f,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> </div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  4.f, 4.f,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  2.f, 1.f,</div><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>  <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  1.f, -2.f,</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  6.f, 4.f</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  };</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  std::vector<float> expectedOutputValues</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  {</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  1.f, 3.f,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  4.f, 3.f,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  4.f, 4.f,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  2.f, 3.f,</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>  <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  1.f, 2.f,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  6.f, 4.f</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  };</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  workloadFactory,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  memoryManager,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  inputOutputShape,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  inputValues,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  expectedOutputValues,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  0.f,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  0,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> |
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| </div> |
| <a id="a95e3411d80e0eac3844844c017f03861"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a95e3411d80e0eac3844844c017f03861">◆ </a></span>BatchNormFloat32Test()</h2> |
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| <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, 4> BatchNormFloat32Test </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></td> |
| <td>)</td> |
| <td></td><td></td> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00207">207</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
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| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> {</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  std::vector<float> inputValues</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  1.f, 4.f,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  4.f, 2.f,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  1.f, 6.f,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  1.f, 1.f,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  4.f, 1.f,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  -2.f, 4.f</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  };</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  std::vector<float> expectedOutputValues</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  1.f, 4.f,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  4.f, 2.f,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  1.f, 6.f,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  3.f, 3.f,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  4.f, 3.f,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  2.f, 4.f</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  };</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  workloadFactory,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  memoryManager,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  inputOutputShape,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  inputValues,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  expectedOutputValues,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  0.f,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  0,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div> |
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| </div> |
| <a id="a40379f76fb69d26e8543dd1494674335"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a40379f76fb69d26e8543dd1494674335">◆ </a></span>BatchNormInt16NhwcTest()</h2> |
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| <div class="memproto"> |
| <table class="memname"> |
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| <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><int16_t, 4> BatchNormInt16NhwcTest </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></td> |
| <td>)</td> |
| <td></td><td></td> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00537">537</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
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| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> {</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span> </div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  std::vector<float> inputValues</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  {</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  1.f, 1.f,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  4.f, 1.f,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  4.f, 4.f,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  2.f, 1.f,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> </div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  1.f, -2.f,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  6.f, 4.f</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  };</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  std::vector<float> expectedOutputValues</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  {</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  1.f, 3.f,</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  4.f, 3.f,</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> </div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  4.f, 4.f,</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  2.f, 3.f,</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  1.f, 2.f,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  6.f, 4.f</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> </div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  workloadFactory,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  memoryManager,</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  inputOutputShape,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  inputValues,</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  expectedOutputValues,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  1.f / 20.f,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  50,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> |
| </div><!-- fragment --> |
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| </div> |
| <a id="aa3fcd011e2fba798b1d5c8d4d2ee9ad8"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#aa3fcd011e2fba798b1d5c8d4d2ee9ad8">◆ </a></span>BatchNormInt16Test()</h2> |
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| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
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| <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><int16_t, 4> BatchNormInt16Test </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> |
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| <td></td> |
| <td>)</td> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00491">491</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
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| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</a>.</p> |
| <div class="fragment"><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>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  std::vector<float> inputValues</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  {</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  1.f, 4.f,</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  4.f, 2.f,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  1.f, 6.f,</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> </div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  1.f, 1.f,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  4.f, 1.f,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  -2.f, 4.f</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  };</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  std::vector<float> expectedOutputValues</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  {</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  1.f, 4.f,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  4.f, 2.f,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  1.f, 6.f,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  3.f, 3.f,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  4.f, 3.f,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  2.f, 4.f</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  };</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  workloadFactory,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  memoryManager,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  inputOutputShape,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  inputValues,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  expectedOutputValues,</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  1.f / 20.f,</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  50,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div> |
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| <a id="a168bb6829b7b1bd091ab3800a055f7ee"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a168bb6829b7b1bd091ab3800a055f7ee">◆ </a></span>BatchNormUint8NhwcTest()</h2> |
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| <div class="memitem"> |
| <div class="memproto"> |
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| <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><uint8_t, 4> BatchNormUint8NhwcTest </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> |
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| <td>)</td> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00445">445</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
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| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> {</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> </div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 3, 2, 2 };</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  std::vector<float> inputValues</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  1.f, 1.f,</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  4.f, 1.f,</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  4.f, 4.f,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  2.f, 1.f,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  1.f, -2.f,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  6.f, 4.f</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  };</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  std::vector<float> expectedOutputValues</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="comment">// Batch 0, Height 0, Width (2) x Channel (2)</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  1.f, 3.f,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  4.f, 3.f,</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">// Batch 0, Height 1, Width (2) x Channel (2)</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  4.f, 4.f,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  2.f, 3.f,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="comment">// Batch 0, Height 2, Width (2) x Channel (2)</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  1.f, 2.f,</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  6.f, 4.f</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="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  workloadFactory,</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  memoryManager,</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  inputOutputShape, inputValues, expectedOutputValues,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  1.f/20.f, 50, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> |
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| <a id="ae90e750efd98b6fb3db4bd586df3daff"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ae90e750efd98b6fb3db4bd586df3daff">◆ </a></span>BatchNormUint8Test()</h2> |
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| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
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| <td class="memname"><a class="el" href="struct_layer_test_result.xhtml">LayerTestResult</a><uint8_t, 4> BatchNormUint8Test </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> |
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| <td>)</td> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00399">399</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
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| <p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::NCHW</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> {</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="comment">// BatchSize: 1</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="comment">// Channels: 2</span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="comment">// Height: 3</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="comment">// Width: 2</span></div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputOutputShape{ 1, 2, 3, 2 };</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  std::vector<float> inputValues</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  {</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  1.f, 4.f,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  4.f, 2.f,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  1.f, 6.f,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> </div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  1.f, 1.f,</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  4.f, 1.f,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  -2.f, 4.f</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  };</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  std::vector<float> expectedOutputValues</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="comment">// Batch 0, Channel 0, Height (3) x Width (2)</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  1.f, 4.f,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  4.f, 2.f,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  1.f, 6.f,</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>  <span class="comment">// Batch 0, Channel 1, Height (3) x Width (2)</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  3.f, 3.f,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  4.f, 3.f,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  2.f, 4.f</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  };</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>  <span class="keywordflow">return</span> BatchNormTestImpl<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  workloadFactory,</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  memoryManager,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  inputOutputShape,</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  inputValues,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  expectedOutputValues,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  1.f / 20.f,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  50,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div> |
| </div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a39988d3dc5c636fa49e8192f26d72554"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a39988d3dc5c636fa49e8192f26d72554">◆ </a></span>CompareBatchNormTest()</h2> |
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| <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,4> CompareBatchNormTest </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"><a class="el" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> & </td> |
| <td class="paramname"><em>refWorkloadFactory</em> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
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| <p class="definition">Definition at line <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml#l00587">587</a> of file <a class="el" href="_batch_normalization_test_impl_8cpp_source.xhtml">BatchNormalizationTestImpl.cpp</a>.</p> |
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| <p class="reference">References <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00019">AllocateAndCopyDataToITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00014">CopyDataFromITensorHandle()</a>, <a class="el" href="_tensor_copy_utils_8cpp_source.xhtml#l00009">CopyDataToITensorHandle()</a>, <a class="el" href="_workload_factory_8cpp_source.xhtml#l01117">IWorkloadFactory::CreateBatchNormalization()</a>, <a class="el" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">IWorkloadFactory::CreateTensorHandle()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00281">BatchNormalizationQueueDescriptor::m_Beta</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00623">BatchNormalizationDescriptor::m_Eps</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00282">BatchNormalizationQueueDescriptor::m_Gamma</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00279">BatchNormalizationQueueDescriptor::m_Mean</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00280">BatchNormalizationQueueDescriptor::m_Variance</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> {</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 2;</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 3;</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 5;</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 3;</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span> </div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo;</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>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {batchSize, channels, height, width};</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorShape[] = {channels};</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>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  tensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, tensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> </div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keyword">auto</span> input = MakeRandomTensor<float, 4>(inputTensorInfo, 21312);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="keyword">auto</span> mean = MakeRandomTensor<float, 1>(tensorInfo, 123);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="keyword">auto</span> variance = MakeRandomTensor<float, 1>(tensorInfo, 234, 0.0f);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keyword">auto</span> beta = MakeRandomTensor<float, 1>(tensorInfo, 123);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keyword">auto</span> gamma = MakeRandomTensor<float, 1>(tensorInfo, 345);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> </div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> ret(outputTensorInfo);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> </div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a> data;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> meanTensor(tensorInfo);</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> varianceTensor(tensorInfo);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> betaTensor(tensorInfo);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> gammaTensor(tensorInfo);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> </div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&meanTensor, &mean[0]);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&varianceTensor, &variance[0]);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&betaTensor, &beta[0]);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&gammaTensor, &gamma[0]);</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>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">m_Mean</a> = &meanTensor;</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">m_Variance</a> = &varianceTensor;</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">m_Beta</a> = &betaTensor;</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  data.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">m_Gamma</a> = &gammaTensor;</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.01f;</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> </div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</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>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">CreateBatchNormalization</a>(data, info);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">CreateBatchNormalization</a>(refData, refInfo);</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> </div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  inputHandle->Allocate();</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  outputHandle->Allocate();</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  inputHandleRef->Allocate();</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  outputHandleRef->Allocate();</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> </div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  workload->Execute();</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  workloadRef->PostAllocationConfigure();</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  workloadRef->Execute();</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span> </div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.outputExpected[0][0][0][0], outputHandleRef.get());</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">return</span> ret;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> }</div><div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_abe1e0d40e23195022c0bc10a8aab55ea"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#abe1e0d40e23195022c0bc10a8aab55ea">armnn::IWorkloadFactory::CreateBatchNormalization</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateBatchNormalization(const BatchNormalizationQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01117">WorkloadFactory.cpp:1117</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_afbe59e02a5464703b865ea1ccfff49fd"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">armnn::BatchNormalizationQueueDescriptor::m_Gamma</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Gamma</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00282">WorkloadData.hpp:282</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_ad5f8f205ba69eb186688ca1c2aec207c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">armnn::BatchNormalizationQueueDescriptor::m_Beta</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Beta</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00281">WorkloadData.hpp:281</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#l00053">Tensor.hpp:53</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a40051a7aa82f25df43cc4244de04a7ec"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">armnn::BatchNormalizationQueueDescriptor::m_Mean</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Mean</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00279">WorkloadData.hpp:279</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00623">Descriptors.hpp:623</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a8cd8696bb773a02714d3fc095794ec54"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">armnn::BatchNormalizationQueueDescriptor::m_Variance</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Variance</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00280">WorkloadData.hpp:280</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div> |
| <div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div> |
| <div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div> |
| <div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo, const bool IsMemoryManaged=true) const =0</div></div> |
| <div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div> |
| <div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> |
| <div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00269">WorkloadData.hpp:269</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="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</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#l00029">LayerTestResult.hpp:29</a></div></div> |
| <div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div> |
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