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<a href="#pub-methods">Public Member Functions</a> &#124;
<a href="#pub-static-methods">Static Public Member Functions</a> </div>
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<div class="title">NEFullyConnectedLayer Class Reference</div> </div>
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<p>Basic function to compute a Fully Connected layer on NEON.
<a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#details">More...</a></p>
<p><code>#include &lt;<a class="el" href="_n_e_fully_connected_layer_8h_source.xhtml">NEFullyConnectedLayer.h</a>&gt;</code></p>
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Collaboration diagram for NEFullyConnectedLayer:</div>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a9845f83f6f3bc45f9fb57ea1345e3dd3"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a9845f83f6f3bc45f9fb57ea1345e3dd3">NEFullyConnectedLayer</a> (std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt; memory_manager=nullptr, <a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> *weights_manager=nullptr)</td></tr>
<tr class="memdesc:a9845f83f6f3bc45f9fb57ea1345e3dd3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor. <a href="#a9845f83f6f3bc45f9fb57ea1345e3dd3">More...</a><br /></td></tr>
<tr class="separator:a9845f83f6f3bc45f9fb57ea1345e3dd3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae8b8211f8ff431633ad55b754638b805"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ae8b8211f8ff431633ad55b754638b805">NEFullyConnectedLayer</a> (const <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;)=delete</td></tr>
<tr class="memdesc:ae8b8211f8ff431633ad55b754638b805"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#ae8b8211f8ff431633ad55b754638b805">More...</a><br /></td></tr>
<tr class="separator:ae8b8211f8ff431633ad55b754638b805"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad5678ca257e666aff1554ea527cffd41"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ad5678ca257e666aff1554ea527cffd41">NEFullyConnectedLayer</a> (<a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;&amp;)=default</td></tr>
<tr class="memdesc:ad5678ca257e666aff1554ea527cffd41"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move constructor. <a href="#ad5678ca257e666aff1554ea527cffd41">More...</a><br /></td></tr>
<tr class="separator:ad5678ca257e666aff1554ea527cffd41"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab6dae67a00e8a7cb4809608be13d2e61"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ab6dae67a00e8a7cb4809608be13d2e61">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;)=delete</td></tr>
<tr class="memdesc:ab6dae67a00e8a7cb4809608be13d2e61"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#ab6dae67a00e8a7cb4809608be13d2e61">More...</a><br /></td></tr>
<tr class="separator:ab6dae67a00e8a7cb4809608be13d2e61"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abebe778a14cc04d5e891c85000bbf75d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#abebe778a14cc04d5e891c85000bbf75d">operator=</a> (<a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;&amp;)=default</td></tr>
<tr class="memdesc:abebe778a14cc04d5e891c85000bbf75d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default move assignment operator. <a href="#abebe778a14cc04d5e891c85000bbf75d">More...</a><br /></td></tr>
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<tr class="memitem:a91fb7694ae938cfec69ff6474451de49"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a91fb7694ae938cfec69ff6474451de49">configure</a> (const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, <a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> fc_info=<a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a>())</td></tr>
<tr class="memdesc:a91fb7694ae938cfec69ff6474451de49"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the input and output tensors. <a href="#a91fb7694ae938cfec69ff6474451de49">More...</a><br /></td></tr>
<tr class="separator:a91fb7694ae938cfec69ff6474451de49"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr>
<tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Run the kernels contained in the function. <a href="#ad1717410afd0be936c6213a63c8005fb">More...</a><br /></td></tr>
<tr class="separator:ad1717410afd0be936c6213a63c8005fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a> () override</td></tr>
<tr class="memdesc:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="mdescLeft">&#160;</td><td class="mdescRight">Prepare the function for executing. <a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">More...</a><br /></td></tr>
<tr class="separator:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classarm__compute_1_1_i_function"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarm__compute_1_1_i_function')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classarm__compute_1_1_i_function.xhtml">IFunction</a></td></tr>
<tr class="memitem:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">~IFunction</a> ()=default</td></tr>
<tr class="memdesc:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <a href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">More...</a><br /></td></tr>
<tr class="separator:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a8da875051f2d75a497fb2de9cdd2e6cb"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classarm__compute_1_1_status.xhtml">Status</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">validate</a> (const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> fc_info=<a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a>())</td></tr>
<tr class="memdesc:a8da875051f2d75a497fb2de9cdd2e6cb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>. <a href="#a8da875051f2d75a497fb2de9cdd2e6cb">More...</a><br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Basic function to compute a Fully Connected layer on NEON. </p>
<p>This function calls the following NEON kernels:</p><ol type="1">
<li><a class="el" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml">NEIm2ColKernel</a> (called when the input comes from a convolutional layer)</li>
<li><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml">NEFullyConnectedLayerReshapeWeights</a> (if <code>are_weights_reshaped</code> is set to false and transpose_weights is set to true ) (called once)</li>
<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_multiply_kernel.xhtml">NEGEMMMatrixMultiplyKernel</a> or <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml">NEGEMMLowpMatrixMultiplyCore</a> (if quantized asymmetric)</li>
<li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml">NEGEMMMatrixAccumulateBiasesKernel</a> or <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml">NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint</a> (if quantized asymmetric) (if <code>biases</code> is not equal to nullptr)</li>
</ol>
<dl class="section note"><dt>Note</dt><dd>The fully connected layer accepts "weights" tensors only with 2 dimensions. </dd></dl>
<p class="definition">Definition at line <a class="el" href="_n_e_fully_connected_layer_8h_source.xhtml#l00114">114</a> of file <a class="el" href="_n_e_fully_connected_layer_8h_source.xhtml">NEFullyConnectedLayer.h</a>.</p>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="a9845f83f6f3bc45f9fb57ea1345e3dd3"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a9845f83f6f3bc45f9fb57ea1345e3dd3">&#9670;&nbsp;</a></span>NEFullyConnectedLayer() <span class="overload">[1/3]</span></h2>
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<td class="memname"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> </td>
<td>(</td>
<td class="paramtype">std::shared_ptr&lt; <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> &gt;&#160;</td>
<td class="paramname"><em>memory_manager</em> = <code>nullptr</code>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> *&#160;</td>
<td class="paramname"><em>weights_manager</em> = <code>nullptr</code>&#160;</td>
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<td></td>
<td>)</td>
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</div><div class="memdoc">
<p>Constructor. </p>
<p class="definition">Definition at line <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00078">78</a> of file <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml">NEFullyConnectedLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; : _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten_kernel(), _convert_weights(), _convert_weights_managed(), _reshape_weights_function(),</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; _reshape_weights_managed_function(), _mm_gemm(<span class="keyword">nullptr</span>, weights_manager), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(),</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; _converted_weights_output(), _reshape_weights_output(), _original_weights(<span class="keyword">nullptr</span>), _are_weights_converted(<span class="keyword">true</span>), _are_weights_reshaped(<span class="keyword">false</span>), _is_fc_after_conv(<span class="keyword">false</span>), _accumulate_biases(<span class="keyword">false</span>),</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; _is_quantized(<span class="keyword">false</span>), _is_prepared(<span class="keyword">false</span>)</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;}</div></div><!-- fragment -->
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<a id="ae8b8211f8ff431633ad55b754638b805"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ae8b8211f8ff431633ad55b754638b805">&#9670;&nbsp;</a></span>NEFullyConnectedLayer() <span class="overload">[2/3]</span></h2>
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<td class="memname"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;&#160;</td>
<td class="paramname"></td><td>)</td>
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<span class="mlabels"><span class="mlabel">delete</span></span> </td>
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<p>Prevent instances of this class from being copied (As this class contains pointers) </p>
</div>
</div>
<a id="ad5678ca257e666aff1554ea527cffd41"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ad5678ca257e666aff1554ea527cffd41">&#9670;&nbsp;</a></span>NEFullyConnectedLayer() <span class="overload">[3/3]</span></h2>
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<td>(</td>
<td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;&amp;&#160;</td>
<td class="paramname"></td><td>)</td>
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<p>Default move constructor. </p>
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<h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a91fb7694ae938cfec69ff6474451de49">&#9670;&nbsp;</a></span>configure()</h2>
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<td class="memname">void configure </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
<td class="paramname"><em>input</em>, </td>
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<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
<td class="paramname"><em>weights</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
<td class="paramname"><em>biases</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *&#160;</td>
<td class="paramname"><em>output</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a>&#160;</td>
<td class="paramname"><em>fc_info</em> = <code><a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a>()</code>&#160;</td>
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<td></td>
<td>)</td>
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<p>Set the input and output tensors. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor. The weights must be 2 dimensional. If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions. If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension. Data type supported: Same as <code>input</code>. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Bias tensor. Can be nullptr. Data type supported: Same as <code>weights</code>, S32 if <code>weights</code> is QASYMM8/QASYMM8_SIGNED. </td></tr>
<tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Destination tensor. Its shape should be equal to the output of a matrix multiplication between:<ul>
<li>The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer</li>
<li>The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer. Data type supported: Same as <code>input</code>. </li>
</ul>
</td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">fc_info</td><td>(Optional) Fully connected layer additional info </td></tr>
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</dl>
<p class="definition">Definition at line <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00141">141</a> of file <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml">NEFullyConnectedLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;{</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="comment">// Perform validate step</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">NEFullyConnectedLayer::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info(),</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(),</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; biases != <span class="keyword">nullptr</span> ? biases-&gt;info() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; output-&gt;info(),</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; fc_info));</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; _are_weights_converted = <span class="keyword">true</span>;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : <span class="keyword">true</span>;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; _is_fc_after_conv = <span class="keyword">true</span>;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; _accumulate_biases = <span class="keyword">false</span>;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; _is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;data_type());</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; <span class="keywordflow">if</span>(_weights_manager)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; {</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a18bd2d54155972d4d740fbba8337a27e">manage</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; }</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="comment">// Configure gemmlowp output</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; _gemmlowp_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(output-&gt;info()-&gt;clone()-&gt;set_is_resizable(<span class="keyword">true</span>).reset_padding().set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; }</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; <span class="comment">// Configure accumulate biases kernel for non quantized asymmetric types</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span> &amp;&amp; !_is_quantized)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; {</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; _accumulate_biases = <span class="keyword">true</span>;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; <span class="comment">// Configure accumulate biases kernel</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; _accumulate_biases_kernel.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aed1adf983092c2f8af29eba1dc29920c">configure</a>(output, biases);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <span class="comment">// With the Fully Connected layer we can have 4 different cases:</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <span class="comment">// 1) Convolution layer -&gt; Fully Connected layer without batches</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <span class="comment">// 2) Fully Connected layer -&gt; Fully Connected layer without batches</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; <span class="comment">// 3) Convolution layer -&gt; Fully Connected layer with batches</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <span class="comment">// 4) Fully Connected layer -&gt; Fully Connected layer with batches</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <span class="keyword">const</span> ITensor *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; <span class="comment">// Check if we have a fully connected layer with batches</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> is_batched_fc_layer = output-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1) &gt; 1;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; _is_fc_after_conv = (<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> &gt;= 4) &amp;&amp; (std::equal(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;tensor_shape().cbegin() + 3,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;tensor_shape().cend(),</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; output-&gt;info()-&gt;tensor_shape().cbegin() + 1));</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; }</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; _is_fc_after_conv = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;num_dimensions() &gt; 1;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="comment">// Reshape weights if needed</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; <span class="keywordflow">if</span>(_weights_manager &amp;&amp; _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>))</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; _reshape_weights_managed_function.<a class="code" href="classarm__compute_1_1weights__transformations_1_1_n_e_fully_connected_layer_reshape_weights_managed.xhtml#afad51fa14022cfbe5a27548024002aa2">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; weights_to_use = _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">acquire</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &amp;_reshape_weights_managed_function);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; }</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; <span class="comment">// Reshape the weights</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; _reshape_weights_function.<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a83a344e60eb7db895953a942abf16628">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &amp;_reshape_weights_output);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; weights_to_use = &amp;_reshape_weights_output;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <span class="comment">// Convert weights if needed</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <span class="keywordflow">if</span>(_is_fc_after_conv &amp;&amp; (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;data_layout() != fc_info.weights_trained_layout))</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <span class="keywordflow">if</span>(_weights_manager &amp;&amp; _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(weights_to_use))</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; _convert_weights_managed.<a class="code" href="classarm__compute_1_1weights__transformations_1_1_n_e_convert_fully_connected_weights_managed.xhtml#a4db9a257c48616afe7fdd7e445fad8fe">configure</a>(weights_to_use,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;tensor_shape(),</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; fc_info.weights_trained_layout);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; weights_to_use = _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">acquire</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &amp;_convert_weights_managed);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <span class="comment">// Convert weights</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; _convert_weights.<a class="code" href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ac0cfd5b64b24f3dfae04ee3d0f258624">configure</a>(weights_to_use,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; &amp;_converted_weights_output,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;tensor_shape(),</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; fc_info.weights_trained_layout);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; weights_to_use = &amp;_converted_weights_output;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; _are_weights_converted = <span class="keyword">false</span>;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; }</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; ITensor *tmp_output = (_is_quantized) ? &amp;_gemmlowp_output : output;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; {</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; <span class="comment">// Fully Connected layer after a Convolution Layer without batches</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; configure_conv_fc(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, weights_to_use, tmp_output);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; }</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; <span class="comment">// Fully Connected layer after a Fully Connected Layer without batches</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; configure_fc_fc(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, weights_to_use, tmp_output);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <span class="comment">// Configure output stage for asymmetric quantized types</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; <span class="keyword">const</span> UniformQuantizationInfo iq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;info()-&gt;quantization_info().uniform();</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; <span class="keyword">const</span> UniformQuantizationInfo wq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <span class="keyword">const</span> UniformQuantizationInfo oq_info = output-&gt;info()-&gt;quantization_info().uniform();</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">float</span> multiplier = (iq_info.scale * wq_info.scale) / oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; int32_t output_multiplier;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; int32_t output_shift;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a63fdf412c27b0151bd4495c64cc112da">quantization::calculate_quantized_multiplier</a>(multiplier, &amp;output_multiplier, &amp;output_shift);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; GEMMLowpOutputStageInfo gemmlowp_output_stage_info;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; gemmlowp_output_stage_info.gemmlowp_shift = output_shift;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; gemmlowp_output_stage_info.type = <a class="code" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a>;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; _gemmlowp_output_stage.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a215ad3877ff40dcc4e6e39f25f2a27f0">configure</a>(&amp;_gemmlowp_output, biases, output, gemmlowp_output_stage_info);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; _gemmlowp_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</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;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160;}</div><div class="ttc" id="namespacearm__compute_xhtml_a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca"><div class="ttname"><a href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a></div><div class="ttdoc">Quantize using a fixed point multiplication.</div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor.cpp:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &amp;allocator, const Coordinates &amp;coords, TensorInfo &amp;sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ac74736e3863207232a23b7181c1d0f44"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">arm_compute::TensorInfo::quantization_info</a></div><div class="ttdeci">QuantizationInfo quantization_info() const override</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00311">TensorInfo.h:311</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div>
<div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml_a1d28dec57cce925ad92342891bd71e7c"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">arm_compute::UniformQuantizationInfo::scale</a></div><div class="ttdeci">float scale</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00064">QuantizationInfo.h:64</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a18bd2d54155972d4d740fbba8337a27e"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a18bd2d54155972d4d740fbba8337a27e">arm_compute::IWeightsManager::manage</a></div><div class="ttdeci">void manage(const ITensor *weights, ITransformWeights *parent=nullptr)</div><div class="ttdoc">Start managing a weights tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00033">IWeightsManager.cpp:33</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div>
<div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div>
<div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml_a706fc156bcd4c45441bcaad05884b57d"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">arm_compute::QuantizationInfo::uniform</a></div><div class="ttdeci">UniformQuantizationInfo uniform() const</div><div class="ttdoc">Return per layer quantization info.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00148">QuantizationInfo.h:148</a></div></div>
<div class="ttc" id="classarm__compute_1_1weights__transformations_1_1_n_e_convert_fully_connected_weights_managed_xhtml_a4db9a257c48616afe7fdd7e445fad8fe"><div class="ttname"><a href="classarm__compute_1_1weights__transformations_1_1_n_e_convert_fully_connected_weights_managed.xhtml#a4db9a257c48616afe7fdd7e445fad8fe">arm_compute::weights_transformations::NEConvertFullyConnectedWeightsManaged::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const TensorShape &amp;original_input_shape, DataLayout data_layout)</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8h_source.xhtml#l00098">NEConvertFullyConnectedWeights.h:98</a></div></div>
<div class="ttc" id="classarm__compute_1_1weights__transformations_1_1_n_e_fully_connected_layer_reshape_weights_managed_xhtml_afad51fa14022cfbe5a27548024002aa2"><div class="ttname"><a href="classarm__compute_1_1weights__transformations_1_1_n_e_fully_connected_layer_reshape_weights_managed.xhtml#afad51fa14022cfbe5a27548024002aa2">arm_compute::weights_transformations::NEFullyConnectedLayerReshapeWeightsManaged::configure</a></div><div class="ttdeci">void configure(const ITensor *input)</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8h_source.xhtml#l00094">NEFullyConnectedLayer.h:94</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_convert_fully_connected_weights_xhtml_ac0cfd5b64b24f3dfae04ee3d0f258624"><div class="ttname"><a href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ac0cfd5b64b24f3dfae04ee3d0f258624">arm_compute::NEConvertFullyConnectedWeights::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const TensorShape &amp;original_input_shape, DataLayout data_layout)</div><div class="ttdoc">Initialize the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00033">NEConvertFullyConnectedWeights.cpp:33</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">Utils.h:1139</a></div></div>
<div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage_xhtml_a215ad3877ff40dcc4e6e39f25f2a27f0"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a215ad3877ff40dcc4e6e39f25f2a27f0">arm_compute::NEGEMMLowpOutputStage::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo &amp;info)</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00086">NEGEMMLowpOutputStage.cpp:86</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_a8da875051f2d75a497fb2de9cdd2e6cb"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">arm_compute::NEFullyConnectedLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEFullyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00275">NEFullyConnectedLayer.cpp:275</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a63fdf412c27b0151bd4495c64cc112da"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a63fdf412c27b0151bd4495c64cc112da">arm_compute::quantization::calculate_quantized_multiplier</a></div><div class="ttdeci">Status calculate_quantized_multiplier(float multiplier, int32_t *quant_multiplier, int32_t *shift)</div><div class="ttdoc">Calculate quantized representation of multiplier.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00038">AsymmHelpers.cpp:38</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NEFullyConnectedLayerReshapeWeights::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00066">NEFullyConnectedLayer.cpp:66</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a1b67d5b720119d50faa286c774579ecc"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">arm_compute::Dimensions&lt; uint32_t &gt;::num_max_dimensions</a></div><div class="ttdeci">static constexpr size_t num_max_dimensions</div><div class="ttdoc">Number of dimensions the tensor has.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00045">Dimensions.h:45</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel_xhtml_aed1adf983092c2f8af29eba1dc29920c"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aed1adf983092c2f8af29eba1dc29920c">arm_compute::NEGEMMMatrixAccumulateBiasesKernel::configure</a></div><div class="ttdeci">void configure(ITensor *accum, const ITensor *biases)</div><div class="ttdoc">Set the accumulate buffer and the biases of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00084">NEGEMMMatrixAccumulateBiasesKernel.cpp:84</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a9b3e3aff065fafe490a5e190bf530ada"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">arm_compute::IWeightsManager::acquire</a></div><div class="ttdeci">ITensor * acquire(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Acquire the requested reshape tensor of the selected weights.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00117">IWeightsManager.cpp:117</a></div></div>
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<p class="reference">References <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00117">IWeightsManager::acquire()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager::are_weights_managed()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00807">FullyConnectedLayerInfo::are_weights_reshaped</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00455">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_asymm_helpers_8cpp_source.xhtml#l00038">arm_compute::quantization::calculate_quantized_multiplier()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00210">Dimensions&lt; T &gt;::cbegin()</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable&lt; T &gt;::clone()</a>, <a class="el" href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00033">NEConvertFullyConnectedWeights::configure()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00066">NEFullyConnectedLayerReshapeWeights::configure()</a>, <a class="el" href="_n_e_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00084">NEGEMMMatrixAccumulateBiasesKernel::configure()</a>, <a class="el" href="_n_e_fully_connected_layer_8h_source.xhtml#l00094">NEFullyConnectedLayerReshapeWeightsManaged::configure()</a>, <a class="el" href="_n_e_convert_fully_connected_weights_8h_source.xhtml#l00098">NEConvertFullyConnectedWeightsManaged::configure()</a>, <a class="el" href="_n_e_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00086">NEGEMMLowpOutputStage::configure()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01948">GEMMLowpOutputStageInfo::gemmlowp_multiplier</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01947">GEMMLowpOutputStageInfo::gemmlowp_offset</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01949">GEMMLowpOutputStageInfo::gemmlowp_shift</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor::info()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator::init()</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00033">IWeightsManager::manage()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00045">Dimensions&lt; uint32_t &gt;::num_max_dimensions</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00065">UniformQuantizationInfo::offset</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00311">TensorInfo::quantization_info()</a>, <a class="el" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::QUANTIZE_DOWN_FIXEDPOINT</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00808">FullyConnectedLayerInfo::retain_internal_weights</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00064">UniformQuantizationInfo::scale</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00806">FullyConnectedLayerInfo::transpose_weights</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01946">GEMMLowpOutputStageInfo::type</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00148">QuantizationInfo::uniform()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00275">NEFullyConnectedLayer::validate()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, and <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00805">FullyConnectedLayerInfo::weights_trained_layout</a>.</p>
<p class="reference">Referenced by <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00067">NERNNLayer::configure()</a>, and <a class="el" href="_n_e_l_s_t_m_layer_8cpp_source.xhtml#l00056">NELSTMLayer::configure()</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#ab6dae67a00e8a7cb4809608be13d2e61">&#9670;&nbsp;</a></span>operator=() <span class="overload">[1/2]</span></h2>
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<td class="memname"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>&amp; operator= </td>
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<td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a> &amp;&#160;</td>
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<p>Prevent instances of this class from being copied (As this class contains pointers) </p>
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<h2 class="memtitle"><span class="permalink"><a href="#abebe778a14cc04d5e891c85000bbf75d">&#9670;&nbsp;</a></span>operator=() <span class="overload">[2/2]</span></h2>
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<td class="memname"><a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>&amp; operator= </td>
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<p>Default move assignment operator. </p>
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<h2 class="memtitle"><span class="permalink"><a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">&#9670;&nbsp;</a></span>prepare()</h2>
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<td class="memname">void prepare </td>
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<p>Prepare the function for executing. </p>
<p>Any one off pre-processing step required by the function is handled here</p>
<dl class="section note"><dt>Note</dt><dd>Prepare stage might not need all the function's buffers' backing memory to be available in order to execute </dd></dl>
<p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p>
<p class="definition">Definition at line <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00417">417</a> of file <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml">NEFullyConnectedLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160;{</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160; {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; <span class="keywordflow">if</span>(!_weights_manager)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; }</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; <span class="keyword">auto</span> release_unused = [](Tensor * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>)</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; {</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; <span class="keywordflow">if</span>(!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>-&gt;is_used())</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>-&gt;allocator()-&gt;free();</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; }</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; };</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; <span class="comment">// Pointer to current weights</span></div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; <span class="keyword">const</span> ITensor *cur_weights = _original_weights;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; <span class="comment">// Reshape of the weights (happens only once)</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; {</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; <span class="keywordflow">if</span>(_weights_manager &amp;&amp; _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(_original_weights))</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; {</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; cur_weights = _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">run</a>(cur_weights, &amp;_reshape_weights_managed_function);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; <span class="comment">// Reshape of the weights (happens only once)</span></div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; {</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; <span class="comment">// Run reshape weights kernel and mark weights as unused</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; _reshape_weights_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; _reshape_weights_function.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; }</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; cur_weights-&gt;mark_as_unused();</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; cur_weights = &amp;_reshape_weights_output;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; }</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; _are_weights_reshaped = <span class="keyword">true</span>;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160; }</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160; <span class="comment">// Convert weights if needed (happens only once)</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; <span class="keywordflow">if</span>(!_are_weights_converted)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; <span class="keywordflow">if</span>(_weights_manager &amp;&amp; _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(cur_weights))</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; _weights_manager-&gt;<a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">run</a>(cur_weights, &amp;_convert_weights_managed);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; }</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; _converted_weights_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; _convert_weights.<a class="code" href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; cur_weights-&gt;mark_as_unused();</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; }</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160;</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; _are_weights_converted = <span class="keyword">true</span>;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; }</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; <span class="comment">// Release reshaped weights if unused</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; release_unused(&amp;_reshape_weights_output);</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160;</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; <span class="comment">// Prepare GEMM prepare and release unused weights</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; <span class="keywordflow">if</span>(!_is_quantized)</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; {</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; _mm_gemm.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; }</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; <span class="comment">// Release converted weights if unused</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; release_unused(&amp;_reshape_weights_output);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; release_unused(&amp;_converted_weights_output);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; }</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160;}</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1a367830ae09bf6138df822888ec1d71"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">arm_compute::test::validation::w</a></div><div class="ttdeci">SimpleTensor&lt; float &gt; w</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00156">DFT.cpp:156</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00466">Error.h:466</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMM::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM.cpp:335</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a73808ac61e51d72c7d6349d6d51dcf37"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">arm_compute::IWeightsManager::run</a></div><div class="ttdeci">ITensor * run(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Run the reshape function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00051">IWeightsManager.cpp:51</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_convert_fully_connected_weights_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEConvertFullyConnectedWeights::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00045">NEConvertFullyConnectedWeights.cpp:45</a></div></div>
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<p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager::are_weights_managed()</a>, <a class="el" href="_error_8h_source.xhtml#l00466">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00162">ITensor::is_used()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM::prepare()</a>, <a class="el" href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder::run()</a>, <a class="el" href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00045">NEConvertFullyConnectedWeights::run()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00051">IWeightsManager::run()</a>, and <a class="el" href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00156">arm_compute::test::validation::w</a>.</p>
<p class="reference">Referenced by <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00120">NERNNLayer::prepare()</a>, and <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00381">NEFullyConnectedLayer::run()</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">&#9670;&nbsp;</a></span>run()</h2>
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<td class="memname">void run </td>
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<p>Run the kernels contained in the function. </p>
<p>For NEON kernels:</p><ul>
<li>Multi-threading is used for the kernels which are parallelisable.</li>
<li>By default std::thread::hardware_concurrency() threads are used.</li>
</ul>
<dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl>
<p>For OpenCL kernels:</p><ul>
<li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li>
<li>The queue is then flushed.</li>
</ul>
<dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd>
<dd>
Will call <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl>
<p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p>
<p class="definition">Definition at line <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00381">381</a> of file <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml">NEFullyConnectedLayer.cpp</a>.</p>
<div class="fragment"><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160;{</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160;</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; <span class="comment">// Linearize input if it comes from a convolutional layer</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; {</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_flatten_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; }</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; <span class="comment">// Run matrix multiply</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; {</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; }</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; {</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; _mm_gemm.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; }</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; <span class="comment">// Accumulate biases if provided</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; _gemmlowp_output_stage.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; }</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; {</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; <span class="keywordflow">if</span>(_accumulate_biases)</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&amp;_accumulate_biases_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; }</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMM::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM.cpp:285</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMMLowpMatrixMultiplyCore::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00479">NEGEMMLowpMatrixMultiplyCore.cpp:479</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &amp;hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEFullyConnectedLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00417">NEFullyConnectedLayer.cpp:417</a></div></div>
<div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div>
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<p class="reference">References <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00417">NEFullyConnectedLayer::prepare()</a>, <a class="el" href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder::run()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM::run()</a>, <a class="el" href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00479">NEGEMMLowpMatrixMultiplyCore::run()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p>
<p class="reference">Referenced by <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00103">NERNNLayer::run()</a>, and <a class="el" href="_n_e_l_s_t_m_layer_8cpp_source.xhtml#l00611">NELSTMLayer::run()</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a8da875051f2d75a497fb2de9cdd2e6cb">&#9670;&nbsp;</a></span>validate()</h2>
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<td class="memname"><a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> validate </td>
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<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
<td class="paramname"><em>input</em>, </td>
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<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
<td class="paramname"><em>weights</em>, </td>
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<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
<td class="paramname"><em>biases</em>, </td>
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<td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *&#160;</td>
<td class="paramname"><em>output</em>, </td>
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<td class="paramtype"><a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a>&#160;</td>
<td class="paramname"><em>fc_info</em> = <code><a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a>()</code>&#160;</td>
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<p>Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml">NEFullyConnectedLayer</a>. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor info. The weights must be 2 dimensional. If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions. If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension. Data type supported: Same as <code>input</code>. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Bias tensor. Can be nullptr. Data type supported: Same as <code>weights</code>, S32 if <code>weights</code> is QASYMM8/QASYMM8_SIGNED. </td></tr>
<tr><td class="paramdir">[in]</td><td class="paramname">output</td><td>Destination tensor info. Its shape should be equal to the output of a matrix multiplication between:<ul>
<li>The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer</li>
<li>The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer. Data type supported: Same as <code>input</code>. </li>
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<tr><td class="paramdir">[in]</td><td class="paramname">fc_info</td><td>(Optional) Fully connected layer additional info</td></tr>
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<dl class="section return"><dt>Returns</dt><dd>a status </dd></dl>
<p class="definition">Definition at line <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00275">275</a> of file <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml">NEFullyConnectedLayer.cpp</a>.</p>
<div class="fragment"><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; <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(fc_info.retain_internal_weights);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">DataType::QASYMM8_SIGNED</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;num_dimensions() &gt; 2);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; <span class="keywordtype">bool</span> weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : <span class="keyword">true</span>;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; <span class="keywordtype">bool</span> is_fc_after_conv = <span class="keyword">true</span>;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;data_type());</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; <span class="keyword">const</span> ITensorInfo &amp;flatten_input = TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;clone()-&gt;set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75">compute_flatten_shape</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>)));</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; <span class="keyword">const</span> ITensorInfo &amp;reshaped_weights = TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;clone()-&gt;set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)));</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; <span class="keyword">const</span> ITensorInfo &amp;converted_weights = weights_reshaped ? TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;clone()-&gt;set_is_resizable(<span class="keyword">true</span>).reset_padding()) : TensorInfo(*reshaped_weights.clone());</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; <span class="keyword">const</span> ITensorInfo &amp;gemmlowp_output = TensorInfo(output-&gt;clone()-&gt;set_is_resizable(<span class="keyword">true</span>).reset_padding().set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</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; <span class="comment">// Configure accumulate biases kernel for non quantized asymmetric types</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span> &amp;&amp; !is_quantized)</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; {</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, biases);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#a67759131249ecc51533fe269d0d90c51">NEGEMMMatrixAccumulateBiasesKernel::validate</a>(output, biases));</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; <span class="comment">// With the Fully Connected layer we can have 4 different cases:</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; <span class="comment">// 1) Convolution layer -&gt; Fully Connected layer without batches</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; <span class="comment">// 2) Fully Connected layer -&gt; Fully Connected layer without batches</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; <span class="comment">// 3) Convolution layer -&gt; Fully Connected layer with batches</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; <span class="comment">// 4) Fully Connected layer -&gt; Fully Connected layer with batches</span></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; <span class="keyword">const</span> ITensorInfo *input_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; <span class="keyword">const</span> ITensorInfo *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; <span class="keyword">const</span> ITensorInfo *tmp_output = (is_quantized) ? &amp;gemmlowp_output : output;</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; <span class="comment">// Check if we have a fully connected layer with batches</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> is_batched_fc_layer = output-&gt;dimension(1) &gt; 1;</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; <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; is_fc_after_conv = (<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> &gt;= 4) &amp;&amp; (std::equal(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;tensor_shape().cbegin() + 3,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;tensor_shape().cend(),</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; output-&gt;tensor_shape().cbegin() + 1));</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; }</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; {</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; is_fc_after_conv = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;num_dimensions() &gt; 1;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; }</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; <span class="keywordflow">if</span>(!weights_reshaped)</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; {</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; <span class="comment">// Validate reshape weights kernel</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEFullyConnectedLayerReshapeWeights::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &amp;reshaped_weights));</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; weights_to_use = &amp;reshaped_weights;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; }</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; <span class="keywordflow">if</span>(is_fc_after_conv &amp;&amp; (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;data_layout() != fc_info.weights_trained_layout))</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; {</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; <span class="comment">// Validate convert weights kernel</span></div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#a01b81c1c60fd95dc360fd7ad680f114b">NEConvertFullyConnectedWeights::validate</a>(weights_to_use,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; &amp;converted_weights,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;tensor_shape(),</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; fc_info.weights_trained_layout));</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; weights_to_use = &amp;converted_weights;</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">if</span>(is_fc_after_conv)</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; <span class="comment">// Fully Connected layer after a Convolution Layer without batches</span></div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>((weights_to_use-&gt;dimension(1) != (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;dimension(0) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;dimension(1) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;dimension(2))));</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; <span class="comment">// Validate flatten kernel</span></div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_flatten_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEFlattenLayerKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &amp;flatten_input));</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; input_to_use = &amp;flatten_input;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; }</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; <span class="comment">// Fully Connected layer after a Fully Connected Layer without batches</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;dimension(0) != weights_to_use-&gt;dimension(1));</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; }</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; <span class="comment">// Validate matrix multiply kernel</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_mm(*input_to_use, *weights_to_use, *tmp_output));</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; <span class="comment">// Validate output stage for asymmetric quantized types</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; <span class="keyword">const</span> UniformQuantizationInfo iq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>-&gt;quantization_info().uniform();</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; <span class="keyword">const</span> UniformQuantizationInfo wq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;quantization_info().uniform();</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; <span class="keyword">const</span> UniformQuantizationInfo oq_info = output-&gt;quantization_info().uniform();</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; <span class="keywordtype">float</span> multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; int32_t output_multiplier;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; int32_t output_shift;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="namespacearm__compute_1_1quantization.xhtml#a63fdf412c27b0151bd4495c64cc112da">quantization::calculate_quantized_multiplier</a>(multiplier, &amp;output_multiplier, &amp;output_shift));</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; GEMMLowpOutputStageInfo gemmlowp_output_stage_info;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; gemmlowp_output_stage_info.gemmlowp_shift = output_shift;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; gemmlowp_output_stage_info.type = <a class="code" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a>;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a8014f142f8a43df8229c8d418f747f8a">NEGEMMLowpOutputStage::validate</a>(&amp;gemmlowp_output, biases, output, gemmlowp_output_stage_info));</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; }</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160;</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160;}</div><div class="ttc" id="namespacearm__compute_xhtml_a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca"><div class="ttname"><a href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a></div><div class="ttdoc">Quantize using a fixed point multiplication.</div></div>
<div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00204">Error.h:204</a></div></div>
<div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00792">Validate.h:792</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div>
<div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00296">Error.h:296</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div>
<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69cb11b5b37f94a6bea9eaad9d13cccf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">arm_compute::misc::shape_calculator::compute_transposed_shape</a></div><div class="ttdeci">TensorShape compute_transposed_shape(const ITensorInfo &amp;input)</div><div class="ttdoc">Calculate the transposed shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00426">ShapeCalculator.h:426</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEFullyConnectedLayerReshapeWeights::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEFullyConnectedLayerRes...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00073">NEFullyConnectedLayer.cpp:73</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div>
<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a83efb6708574e67d13965bcd2059ad75"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75">arm_compute::misc::shape_calculator::compute_flatten_shape</a></div><div class="ttdeci">TensorShape compute_flatten_shape(const ITensorInfo *input)</div><div class="ttdoc">Calculate the flattened output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00587">ShapeCalculator.h:587</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00152">Error.h:152</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">Utils.h:1139</a></div></div>
<div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage_xhtml_a8014f142f8a43df8229c8d418f747f8a"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a8014f142f8a43df8229c8d418f747f8a">arm_compute::NEGEMMLowpOutputStage::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &amp;info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMLowpOutputStage.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00142">NEGEMMLowpOutputStage.cpp:142</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_convert_fully_connected_weights_xhtml_a01b81c1c60fd95dc360fd7ad680f114b"><div class="ttname"><a href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#a01b81c1c60fd95dc360fd7ad680f114b">arm_compute::NEConvertFullyConnectedWeights::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const TensorShape &amp;original_input_shape, DataLayout data_layout)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEConvertFullyConnectedW...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00039">NEConvertFullyConnectedWeights.cpp:39</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel_xhtml_a67759131249ecc51533fe269d0d90c51"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#a67759131249ecc51533fe269d0d90c51">arm_compute::NEGEMMMatrixAccumulateBiasesKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *accum, const ITensorInfo *biases)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMMatrixAccumulateBi...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00100">NEGEMMMatrixAccumulateBiasesKernel.cpp:100</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a63fdf412c27b0151bd4495c64cc112da"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a63fdf412c27b0151bd4495c64cc112da">arm_compute::quantization::calculate_quantized_multiplier</a></div><div class="ttdeci">Status calculate_quantized_multiplier(float multiplier, int32_t *quant_multiplier, int32_t *shift)</div><div class="ttdoc">Calculate quantized representation of multiplier.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00038">AsymmHelpers.cpp:38</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">arm_compute::DataType::QASYMM8_SIGNED</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number signed</div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_flatten_layer_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_flatten_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEFlattenLayerKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEFlattenLayerKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_flatten_layer_kernel_8cpp_source.xhtml#l00095">NEFlattenLayerKernel.cpp:95</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a1b67d5b720119d50faa286c774579ecc"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">arm_compute::Dimensions&lt; uint32_t &gt;::num_max_dimensions</a></div><div class="ttdeci">static constexpr size_t num_max_dimensions</div><div class="ttdoc">Number of dimensions the tensor has.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00045">Dimensions.h:45</a></div></div>
</div><!-- fragment -->
<p class="reference">References <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00807">FullyConnectedLayerInfo::are_weights_reshaped</a>, <a class="el" href="_error_8h_source.xhtml#l00296">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00792">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_validate_8h_source.xhtml#l00545">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>, <a class="el" href="_validate_8h_source.xhtml#l00163">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="_error_8h_source.xhtml#l00152">ARM_COMPUTE_UNUSED</a>, <a class="el" href="_asymm_helpers_8cpp_source.xhtml#l00038">arm_compute::quantization::calculate_quantized_multiplier()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00210">Dimensions&lt; T &gt;::cbegin()</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable&lt; T &gt;::clone()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00587">arm_compute::misc::shape_calculator::compute_flatten_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00426">arm_compute::misc::shape_calculator::compute_transposed_shape()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01948">GEMMLowpOutputStageInfo::gemmlowp_multiplier</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01947">GEMMLowpOutputStageInfo::gemmlowp_offset</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01949">GEMMLowpOutputStageInfo::gemmlowp_shift</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00045">Dimensions&lt; uint32_t &gt;::num_max_dimensions</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00065">UniformQuantizationInfo::offset</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::QASYMM8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">arm_compute::QASYMM8_SIGNED</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::QUANTIZE_DOWN_FIXEDPOINT</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00808">FullyConnectedLayerInfo::retain_internal_weights</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00064">UniformQuantizationInfo::scale</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00806">FullyConnectedLayerInfo::transpose_weights</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l01946">GEMMLowpOutputStageInfo::type</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00148">QuantizationInfo::uniform()</a>, <a class="el" href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00039">NEConvertFullyConnectedWeights::validate()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00073">NEFullyConnectedLayerReshapeWeights::validate()</a>, <a class="el" href="_n_e_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00100">NEGEMMMatrixAccumulateBiasesKernel::validate()</a>, <a class="el" href="_n_e_flatten_layer_kernel_8cpp_source.xhtml#l00095">NEFlattenLayerKernel::validate()</a>, <a class="el" href="_n_e_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00142">NEGEMMLowpOutputStage::validate()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, and <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00805">FullyConnectedLayerInfo::weights_trained_layout</a>.</p>
<p class="reference">Referenced by <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00141">NEFullyConnectedLayer::configure()</a>, <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00042">NERNNLayer::validate()</a>, and <a class="el" href="_n_e_l_s_t_m_layer_8cpp_source.xhtml#l00381">NELSTMLayer::validate()</a>.</p>
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<hr/>The documentation for this class was generated from the following files:<ul>
<li>arm_compute/runtime/NEON/functions/<a class="el" href="_n_e_fully_connected_layer_8h_source.xhtml">NEFullyConnectedLayer.h</a></li>
<li>src/runtime/NEON/functions/<a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml">NEFullyConnectedLayer.cpp</a></li>
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