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<div class="title">FullyConnectedTestImpl.hpp File Reference</div> </div>
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<div class="textblock"><code>#include &quot;<a class="el" href="_layer_test_result_8hpp_source.html">LayerTestResult.hpp</a>&quot;</code><br />
<code>#include &lt;<a class="el" href="_resolve_type_8hpp_source.html">ResolveType.hpp</a>&gt;</code><br />
<code>#include &lt;<a class="el" href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.html">armnn/backends/IBackendInternal.hpp</a>&gt;</code><br />
<code>#include &lt;<a class="el" href="_workload_factory_8hpp_source.html">backendsCommon/WorkloadFactory.hpp</a>&gt;</code><br />
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<p><a href="_fully_connected_test_impl_8hpp_source.html">Go to the source code of this file.</a></p>
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Functions</h2></td></tr>
<tr class="memitem:a25b72d9cbe9cca2c89ba997e6f2cfb87"><td class="memTemplParams" colspan="2">template&lt;armnn::DataType ArmnnType, typename T = armnn::ResolveType&lt;ArmnnType&gt;&gt; </td></tr>
<tr class="memitem:a25b72d9cbe9cca2c89ba997e6f2cfb87"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.html">LayerTestResult</a>&lt; T, 2 &gt;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_fully_connected_test_impl_8hpp.html#a25b72d9cbe9cca2c89ba997e6f2cfb87">FullyConnectedTest</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled)</td></tr>
<tr class="separator:a25b72d9cbe9cca2c89ba997e6f2cfb87"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9aa238fbd4c6a6d1259b31d2a51c93b8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.html">LayerTestResult</a>&lt; float, 2 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_fully_connected_test_impl_8hpp.html#a9aa238fbd4c6a6d1259b31d2a51c93b8">FullyConnectedFloat32Test</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool biasEnabled, bool transposeWeights)</td></tr>
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<tr class="memitem:a0fb6957126b671361ccdd80f3549faa9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="struct_layer_test_result.html">LayerTestResult</a>&lt; float, 2 &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_fully_connected_test_impl_8hpp.html#a0fb6957126b671361ccdd80f3549faa9">FullyConnectedLargeTest</a> (<a class="el" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a> &amp;workloadFactory, const <a class="el" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;memoryManager, bool transposeWeights)</td></tr>
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<h2 class="groupheader">Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a9aa238fbd4c6a6d1259b31d2a51c93b8">&#9670;&nbsp;</a></span>FullyConnectedFloat32Test()</h2>
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<td class="memname"><a class="el" href="struct_layer_test_result.html">LayerTestResult</a>&lt;float, 2&gt; FullyConnectedFloat32Test </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a> &amp;&#160;</td>
<td class="paramname"><em>workloadFactory</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
<td class="paramname"><em>memoryManager</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>biasEnabled</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>transposeWeights</em>&#160;</td>
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<td>)</td>
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<p class="definition">Definition at line <a class="el" href="_fully_connected_test_impl_8cpp_source.html#l00247">247</a> of file <a class="el" href="_fully_connected_test_impl_8cpp_source.html">FullyConnectedTestImpl.cpp</a>.</p>
<p class="reference">References <a class="el" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_layer_test_result_8hpp_source.html#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>, and <a class="el" href="_descriptors_8cpp_source.html#l00342">armnn::swap()</a>.</p>
<div class="fragment"><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160;{</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> weightsDesc;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> biasesDesc;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels };</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { inputChannels, outputChannels };</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; {</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; <a class="code" href="namespacearmnn.html#a14d7f180bf51e86850305965c3707e07">std::swap</a>(weightsShape[0], weightsShape[1]);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; }</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { outputChannels };</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(2, outputShape, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; weightsDesc = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(2, weightsShape, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; biasesDesc = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(1, biasShape, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 2&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; boost::multi_array&lt;float, 4&gt; input = MakeTensor&lt;float, 4&gt;(inputTensorInfo, std::vector&lt;float&gt;(</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; {</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; 1.0f, 2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; })</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; );</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; boost::multi_array&lt;float, 2&gt; weights = MakeTensor&lt;float, 2&gt;(weightsDesc, std::vector&lt;float&gt;(</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; {</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; .5f, 2.f, .5f,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; .5f, 2.f, 1.f,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; .5f, 2.f, 2.f,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; .5f, 2.f, 3.f,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; .5f, 2.f, 4.f</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; }));</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; <span class="keywordflow">if</span> (transposeWeights)</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; {</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; weights = MakeTensor&lt;float, 2&gt;(weightsDesc, std::vector&lt;float&gt;(</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; .5f, .5f, .5f, .5f, .5f,</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; 2.f, 2.f, 2.f, 2.f, 2.f,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; .5f, 1.f, 2.f, 3.f, 4.f</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; }));</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; }</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160;</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; std::vector&lt;float&gt; biasValues({0.f, 0.f, 0.f});</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; {</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; biasValues = std::vector&lt;float&gt;({10.f, 20.f, 30.f});</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; boost::multi_array&lt;float, 1&gt; bias = MakeTensor&lt;float, 1&gt;(biasesDesc, biasValues);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; result = SimpleFullyConnectedTestImpl&lt;float&gt;(</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; workloadFactory,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; memoryManager,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; weightsDesc, biasesDesc,</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; weights, bias, input,</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; biasEnabled, transposeWeights</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; );</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; result.outputExpected = MakeTensor&lt;float, 2&gt;(outputTensorInfo, std::vector&lt;float&gt;(</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; {</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; 0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; 2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; 0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; 2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; 10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; 2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2]</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; })</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; );</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div>
<div class="ttc" id="struct_layer_test_result_html"><div class="ttname"><a href="struct_layer_test_result.html">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.html#l00029">LayerTestResult.hpp:29</a></div></div>
<div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0fb6957126b671361ccdd80f3549faa9">&#9670;&nbsp;</a></span>FullyConnectedLargeTest()</h2>
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<td class="memname"><a class="el" href="struct_layer_test_result.html">LayerTestResult</a>&lt;float, 2&gt; FullyConnectedLargeTest </td>
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<td class="paramtype"><a class="el" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a> &amp;&#160;</td>
<td class="paramname"><em>workloadFactory</em>, </td>
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<td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
<td class="paramname"><em>memoryManager</em>, </td>
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<td class="paramtype">bool&#160;</td>
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<p class="definition">Definition at line <a class="el" href="_fully_connected_test_impl_8cpp_source.html#l00344">344</a> of file <a class="el" href="_fully_connected_test_impl_8cpp_source.html">FullyConnectedTestImpl.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160;{</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; <span class="keywordflow">return</span> FullyConnectedLargeTestCommon&lt;armnn::DataType::Float32&gt;(workloadFactory, memoryManager, transposeWeights);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160;}</div></div><!-- fragment -->
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<h2 class="memtitle"><span class="permalink"><a href="#a25b72d9cbe9cca2c89ba997e6f2cfb87">&#9670;&nbsp;</a></span>FullyConnectedTest()</h2>
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<td class="memname"><a class="el" href="struct_layer_test_result.html">LayerTestResult</a>&lt;T, 2&gt; FullyConnectedTest </td>
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<td class="paramtype">const <a class="el" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &amp;&#160;</td>
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<p class="definition">Definition at line <a class="el" href="_fully_connected_test_impl_8cpp_source.html#l00071">71</a> of file <a class="el" href="_fully_connected_test_impl_8cpp_source.html">FullyConnectedTestImpl.cpp</a>.</p>
<p class="reference">References <a class="el" href="_layer_support_rules_8hpp_source.html#l00014">armnn::GetBiasTypeFromWeightsType()</a>, <a class="el" href="_layer_test_result_8hpp_source.html#l00041">LayerTestResult&lt; T, n &gt;::outputExpected</a>, <a class="el" href="_tensor_8cpp_source.html#l00259">TensorInfo::SetQuantizationScale()</a>, and <a class="el" href="_cl_layer_tests_8cpp_source.html#l00176">true</a>.</p>
<div class="fragment"><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;{</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3u;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2u;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; 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="l00081"></a><span class="lineno"> 81</span>&#160;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 2u;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.1f);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; inputTensorInfo.SetQuantizationOffset(63);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo({ 1, outputChannels }, ArmnnType);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(5.f);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> weightsDesc({ outputChannels, inputSize }, ArmnnType);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; weightsDesc.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(0.2f);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; weightsDesc.SetQuantizationOffset(93);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> biasesDesc({ outputChannels }, <a class="code" href="namespacearmnn.html#a83c4a275acf59f62b8387f389d0929d5">GetBiasTypeFromWeightsType</a>(weightsDesc.GetDataType()).value());</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; biasesDesc.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale());</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; biasesDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 2&gt;</a> result(outputTensorInfo);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="keyword">auto</span> input = MakeTensor&lt;T, 4&gt;(inputTensorInfo, ConvertToDataType&lt;ArmnnType&gt;(</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; -1.2f, 6.1f, -3.5f,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; 18.8f, -5.5f, 2.9f</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; },</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; inputTensorInfo));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="keyword">auto</span> weights = MakeTensor&lt;T, 2&gt;(weightsDesc, ConvertToDataType&lt;ArmnnType&gt;(</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; },</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; weightsDesc));</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keyword">auto</span> bias = MakeTensor&lt;int32_t, 1&gt;(biasesDesc, std::vector&lt;int32_t&gt;{9250, 67500});</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; result = SimpleFullyConnectedTestImpl&lt;T&gt;(</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; workloadFactory,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; memoryManager,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; inputTensorInfo, outputTensorInfo,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; weightsDesc, biasesDesc,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; weights, bias, input,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; biasEnabled, <a class="code" href="_cl_layer_tests_8cpp.html#a37f1c3ccc9fc906be85185350dd83d48">true</a></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; );</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; result.outputExpected = MakeTensor&lt;T, 2&gt;(outputTensorInfo,</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; ConvertToDataType&lt;ArmnnType&gt;({80.f, 1460.f}, outputTensorInfo));</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; result.outputExpected = MakeTensor&lt;T, 2&gt;(outputTensorInfo,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; ConvertToDataType&lt;ArmnnType&gt;({-107.04f, 110.f}, outputTensorInfo));</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; }</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div>
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<div class="ttc" id="_cl_layer_tests_8cpp_html_a37f1c3ccc9fc906be85185350dd83d48"><div class="ttname"><a href="_cl_layer_tests_8cpp.html#a37f1c3ccc9fc906be85185350dd83d48">true</a></div><div class="ttdeci">DataLayout::NCHW DataLayout::NCHW DataLayout::NHWC DataLayout::NHWC true</div><div class="ttdef"><b>Definition:</b> <a href="_cl_layer_tests_8cpp_source.html#l00176">ClLayerTests.cpp:176</a></div></div>
<div class="ttc" id="namespacearmnn_html_a83c4a275acf59f62b8387f389d0929d5"><div class="ttname"><a href="namespacearmnn.html#a83c4a275acf59f62b8387f389d0929d5">armnn::GetBiasTypeFromWeightsType</a></div><div class="ttdeci">armnn::Optional&lt; armnn::DataType &gt; GetBiasTypeFromWeightsType(armnn::Optional&lt; armnn::DataType &gt; weightsType)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_rules_8hpp_source.html#l00014">LayerSupportRules.hpp:14</a></div></div>
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