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| <div class="title">CLFullyConnectedLayer Class Reference</div> </div> |
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| |
| <p>Basic function to compute a Fully Connected layer on OpenCL. |
| <a href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_c_l_fully_connected_layer_8h_source.xhtml">CLFullyConnectedLayer.h</a>></code></p> |
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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:a5f5f7b33836b21106eb116050ddf1401"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a5f5f7b33836b21106eb116050ddf1401">CLFullyConnectedLayer</a> (std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > memory_manager=nullptr)</td></tr> |
| <tr class="memdesc:a5f5f7b33836b21106eb116050ddf1401"><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#a5f5f7b33836b21106eb116050ddf1401">More...</a><br /></td></tr> |
| <tr class="separator:a5f5f7b33836b21106eb116050ddf1401"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a8dcd28ed9671e3e8d6bb5a8b6e35e21c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8dcd28ed9671e3e8d6bb5a8b6e35e21c">CLFullyConnectedLayer</a> (const <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a8dcd28ed9671e3e8d6bb5a8b6e35e21c"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a8dcd28ed9671e3e8d6bb5a8b6e35e21c">More...</a><br /></td></tr> |
| <tr class="separator:a8dcd28ed9671e3e8d6bb5a8b6e35e21c"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a306b9cb4657550df587d17d095e4c586"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a306b9cb4657550df587d17d095e4c586">CLFullyConnectedLayer</a> (<a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a306b9cb4657550df587d17d095e4c586"><td class="mdescLeft"> </td><td class="mdescRight">Default move constructor. <a href="#a306b9cb4657550df587d17d095e4c586">More...</a><br /></td></tr> |
| <tr class="separator:a306b9cb4657550df587d17d095e4c586"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a1bee19103166ceb1eedef2e6a4fe6515"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a1bee19103166ceb1eedef2e6a4fe6515">operator=</a> (const <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:a1bee19103166ceb1eedef2e6a4fe6515"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#a1bee19103166ceb1eedef2e6a4fe6515">More...</a><br /></td></tr> |
| <tr class="separator:a1bee19103166ceb1eedef2e6a4fe6515"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a8a5afe02cef06665b7d7349a8ecbd109"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8a5afe02cef06665b7d7349a8ecbd109">operator=</a> (<a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a8a5afe02cef06665b7d7349a8ecbd109"><td class="mdescLeft"> </td><td class="mdescRight">Default move assignment operator. <a href="#a8a5afe02cef06665b7d7349a8ecbd109">More...</a><br /></td></tr> |
| <tr class="separator:a8a5afe02cef06665b7d7349a8ecbd109"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ab205e8e07c4eff3197d0c8cc85a4488d"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ab205e8e07c4eff3197d0c8cc85a4488d">configure</a> (const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</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:ab205e8e07c4eff3197d0c8cc85a4488d"><td class="mdescLeft"> </td><td class="mdescRight">Set the input and output tensors. <a href="#ab205e8e07c4eff3197d0c8cc85a4488d">More...</a><br /></td></tr> |
| <tr class="separator:ab205e8e07c4eff3197d0c8cc85a4488d"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr> |
| <tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft"> </td><td class="mdescRight">Run the kernels contained in the function. <a href="#ad1717410afd0be936c6213a63c8005fb">More...</a><br /></td></tr> |
| <tr class="separator:ad1717410afd0be936c6213a63c8005fb"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a> () override</td></tr> |
| <tr class="memdesc:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="mdescLeft"> </td><td class="mdescRight">Prepare the function for executing. <a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">More...</a><br /></td></tr> |
| <tr class="separator:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="inherit_header pub_methods_classarm__compute_1_1_i_function"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarm__compute_1_1_i_function')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarm__compute_1_1_i_function.xhtml">IFunction</a></td></tr> |
| <tr class="memitem:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">~IFunction</a> ()=default</td></tr> |
| <tr class="memdesc:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">More...</a><br /></td></tr> |
| <tr class="separator:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2"> </td></tr> |
| </table><table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a> |
| Static Public Member Functions</h2></td></tr> |
| <tr class="memitem:a8da875051f2d75a497fb2de9cdd2e6cb"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_c_l_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"> </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_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>. <a href="#a8da875051f2d75a497fb2de9cdd2e6cb">More...</a><br /></td></tr> |
| <tr class="separator:a8da875051f2d75a497fb2de9cdd2e6cb"><td class="memSeparator" colspan="2"> </td></tr> |
| </table> |
| <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> |
| <div class="textblock"><p>Basic function to compute a Fully Connected layer on OpenCL. </p> |
| <p>This function calls the following OpenCL kernels:</p> |
| <ol type="1"> |
| <li><a class="el" href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml">CLIm2ColKernel</a> (called when the input comes from a convolutional layer)</li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer_reshape_weights.xhtml">CLFullyConnectedLayerReshapeWeights</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_c_l_g_e_m_m_matrix_multiply_kernel.xhtml">CLGEMMMatrixMultiplyKernel</a> or <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml">CLGEMMLowpMatrixMultiplyCore</a> (if quantized asymmetric)</li> |
| <li><a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_accumulate_biases_kernel.xhtml">CLGEMMMatrixAccumulateBiasesKernel</a> or <a class="el" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml">CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint</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="_c_l_fully_connected_layer_8h_source.xhtml#l00075">75</a> of file <a class="el" href="_c_l_fully_connected_layer_8h_source.xhtml">CLFullyConnectedLayer.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="a5f5f7b33836b21106eb116050ddf1401"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a5f5f7b33836b21106eb116050ddf1401">◆ </a></span>CLFullyConnectedLayer() <span class="overload">[1/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > </td> |
| <td class="paramname"><em>memory_manager</em> = <code>nullptr</code></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Constructor. </p> |
| |
| <p class="definition">Definition at line <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00079">79</a> of file <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml">CLFullyConnectedLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  : _memory_group(memory_manager), _convert_weights(), _flatten_layer(), _reshape_weights_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _are_weights_converted(<span class="keyword">true</span>), _are_weights_reshaped(<span class="keyword">true</span>),</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  _is_fc_after_conv(<span class="keyword">true</span>), _accumulate_biases(<span class="keyword">false</span>), _is_quantized(<span class="keyword">false</span>), _is_prepared(<span class="keyword">false</span>), _original_weights(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> {</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <a id="a8dcd28ed9671e3e8d6bb5a8b6e35e21c"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a8dcd28ed9671e3e8d6bb5a8b6e35e21c">◆ </a></span>CLFullyConnectedLayer() <span class="overload">[2/3]</span></h2> |
| |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
| </tr> |
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| </div><div class="memdoc"> |
| |
| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
| |
| </div> |
| </div> |
| <a id="a306b9cb4657550df587d17d095e4c586"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a306b9cb4657550df587d17d095e4c586">◆ </a></span>CLFullyConnectedLayer() <span class="overload">[3/3]</span></h2> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Default move constructor. </p> |
| |
| </div> |
| </div> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a id="ab205e8e07c4eff3197d0c8cc85a4488d"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ab205e8e07c4eff3197d0c8cc85a4488d">◆ </a></span>configure()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname">void configure </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> * </td> |
| <td class="paramname"><em>input</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> * </td> |
| <td class="paramname"><em>weights</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> * </td> |
| <td class="paramname"><em>biases</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> * </td> |
| <td class="paramname"><em>output</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> </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> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
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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/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>input</code>. </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> |
| </table> |
| </dd> |
| </dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00140">140</a> of file <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml">CLFullyConnectedLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="comment">// Perform validate step</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">CLFullyConnectedLayer::validate</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(),</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  biases != <span class="keyword">nullptr</span> ? biases-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  fc_info));</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  _are_weights_converted = <span class="keyword">true</span>;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  _are_weights_reshaped = fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a11851649b6a7cd12ae25cf72b769cfb9">transpose_weights</a> ? fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a146a7be9d813ad80abb72a0bf6566cbc">are_weights_reshaped</a> : <span class="keyword">true</span>;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  _is_fc_after_conv = <span class="keyword">true</span>;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  _accumulate_biases = <span class="keyword">false</span>;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  _is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  _is_prepared = fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a4f87c5060cca01305f94a9d2f10e9d83">retain_internal_weights</a>;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="comment">// Configure gemmlowp output</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  _gemmlowp_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->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="l00164"></a><span class="lineno"> 164</span>  }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="comment">// Configure accumulate biases kernel for non quantized asymmetric types</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span> && !_is_quantized)</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  {</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, biases);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  _accumulate_biases = <span class="keyword">true</span>;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="comment">// Configure accumulate biases kernel</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  _accumulate_biases_kernel.<a class="code" href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">set_target</a>(<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().target());</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  _accumulate_biases_kernel.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#acd42d73bdf14e61d6414450f134e9651">configure</a>(output, biases);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// With the Fully Connected layer we can have 4 different cases:</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// 1) Convolution layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// 2) Fully Connected layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// 3) Convolution layer -> Fully Connected layer with batches</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="comment">// 4) Fully Connected layer -> Fully Connected layer with batches</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Check if we have a fully connected layer with batches</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_batched_fc_layer = output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) > 1;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  _is_fc_after_conv = (<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> >= 4) && (std::equal(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">cbegin</a>() + 3,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#afde273ebfa15fe83c690ad5cf6693c9f">cend</a>(),</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">cbegin</a>() + 1));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  _is_fc_after_conv = input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  }</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="comment">// Reshape weights if needed</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="comment">// Reshape the weights</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  _reshape_weights_kernel.<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer_reshape_weights.xhtml#a074e10cfb217e657b9e81adeca2abc68">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_reshape_weights_output);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  weights_to_use = &_reshape_weights_output;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="comment">// Convert weights if needed</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">if</span>(_is_fc_after_conv && (input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() != fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a89d0ed86875fd3fb395899187c84bc2f">weights_trained_layout</a>))</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// Convert weights</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  _convert_weights.<a class="code" href="classarm__compute_1_1_c_l_convert_fully_connected_weights.xhtml#a52184fd44c124af2e50bdb1649781782">configure</a>(weights_to_use,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  &_converted_weights_output,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(),</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a89d0ed86875fd3fb395899187c84bc2f">weights_trained_layout</a>);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  weights_to_use = &_converted_weights_output;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  _are_weights_converted = <span class="keyword">false</span>;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  }</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="comment">// Configure fc core</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *tmp_output = (_is_quantized) ? &_gemmlowp_output : output;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  {</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="comment">// Fully Connected layer after a Convolution Layer without batches</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  configure_conv_fc(input, weights_to_use, tmp_output, fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a4f87c5060cca01305f94a9d2f10e9d83">retain_internal_weights</a>);</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="comment">// Fully Connected layer after a Fully Connected Layer without batches</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  configure_fc_fc(input, weights_to_use, tmp_output, fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a4f87c5060cca01305f94a9d2f10e9d83">retain_internal_weights</a>);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  }</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="comment">// Configure output stage for asymmetric quantized types</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> iq_info = input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> wq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><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="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> oq_info = output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordtype">float</span> multiplier = iq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> * wq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> / oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordtype">int</span> output_multiplier;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordtype">int</span> output_shift;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a22032f9cf47deae265eafb65ff55b594">quantization::calculate_quantized_multiplier_less_than_one</a>(multiplier, &output_multiplier, &output_shift);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  _gemmlowp_output_stage.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml#a15da37f661fdcd81c1f25c5d6bdc6abd">configure</a>(&_gemmlowp_output, biases, output, output_multiplier, output_shift, oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  _gemmlowp_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  }</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> }</div><div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a22032f9cf47deae265eafb65ff55b594"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a22032f9cf47deae265eafb65ff55b594">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a></div><div class="ttdeci">arm_compute::Status calculate_quantized_multiplier_less_than_one(float multiplier, int *quant_multiplier, int *right_shift)</div><div class="ttdoc">Calculate quantized representation of multiplier with value less than one.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00035">AsymmHelpers.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a5befbfaf6bc224eabc58b5e88b1de6d1"><div class="ttname"><a href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00543">Validate.h:543</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a4f87c5060cca01305f94a9d2f10e9d83"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a4f87c5060cca01305f94a9d2f10e9d83">arm_compute::FullyConnectedLayerInfo::retain_internal_weights</a></div><div class="ttdeci">bool retain_internal_weights</div><div class="ttdoc">Retain internal reshaped weights.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00798">Types.h:798</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div> |
| <div class="ttc" id="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#l00293">TensorInfo.h:293</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point_xhtml_a15da37f661fdcd81c1f25c5d6bdc6abd"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml#a15da37f661fdcd81c1f25c5d6bdc6abd">arm_compute::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min=0, int max=0)</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00047">CLGEMMLowpOutputStage.cpp:47</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</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#l00327">Error.h:327</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml">arm_compute::UniformQuantizationInfo</a></div><div class="ttdoc">Quantization info when assuming per layer quantization.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00039">QuantizationInfo.h:39</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#l00061">QuantizationInfo.h:61</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</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_c_l_g_e_m_m_matrix_accumulate_biases_kernel_xhtml_acd42d73bdf14e61d6414450f134e9651"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#acd42d73bdf14e61d6414450f134e9651">arm_compute::CLGEMMMatrixAccumulateBiasesKernel::configure</a></div><div class="ttdeci">void configure(ICLTensor *accum, const ICLTensor *biases)</div><div class="ttdoc">Set the accumulate buffer and the biases of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00079">CLGEMMMatrixAccumulateBiasesKernel.cpp:79</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_fully_connected_layer_xhtml_a8da875051f2d75a497fb2de9cdd2e6cb"><div class="ttname"><a href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">arm_compute::CLFullyConnectedLayer::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 CLFullyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00249">CLFullyConnectedLayer.cpp:249</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a146a7be9d813ad80abb72a0bf6566cbc"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a146a7be9d813ad80abb72a0bf6566cbc">arm_compute::FullyConnectedLayerInfo::are_weights_reshaped</a></div><div class="ttdeci">bool are_weights_reshaped</div><div class="ttdoc">Reshape the weights tensor if false.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00797">Types.h:797</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#l00134">QuantizationInfo.h:134</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a3f3e1a3200223e6a304a533b1016e749"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">arm_compute::ITensorInfo::quantization_info</a></div><div class="ttdeci">virtual QuantizationInfo quantization_info() const =0</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</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#l01030">Utils.h:1030</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_afde273ebfa15fe83c690ad5cf6693c9f"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#afde273ebfa15fe83c690ad5cf6693c9f">arm_compute::Dimensions::cend</a></div><div class="ttdeci">std::array< T, num_max_dimensions >::const_iterator cend() const</div><div class="ttdoc">Returns a read-only (constant) iterator that points one past the last element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00234">Dimensions.h:234</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a8c9efb4e1ad142d58d65af400f20217d"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">arm_compute::Dimensions::cbegin</a></div><div class="ttdeci">std::array< T, num_max_dimensions >::const_iterator cbegin() const</div><div class="ttdoc">Returns a read-only (constant) iterator that points to the first element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00210">Dimensions.h:210</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_i_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_tensor.xhtml">arm_compute::ICLTensor</a></div><div class="ttdoc">Interface for OpenCL tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_tensor_8h_source.xhtml#l00042">ICLTensor.h:42</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a89d0ed86875fd3fb395899187c84bc2f"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a89d0ed86875fd3fb395899187c84bc2f">arm_compute::FullyConnectedLayerInfo::weights_trained_layout</a></div><div class="ttdeci">DataLayout weights_trained_layout</div><div class="ttdoc">Layout that the weights have been trained with.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00795">Types.h:795</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_convert_fully_connected_weights_xhtml_a52184fd44c124af2e50bdb1649781782"><div class="ttname"><a href="classarm__compute_1_1_c_l_convert_fully_connected_weights.xhtml#a52184fd44c124af2e50bdb1649781782">arm_compute::CLConvertFullyConnectedWeights::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const TensorShape &original_input_shape, DataLayout data_layout)</div><div class="ttdoc">Initialize the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_convert_fully_connected_weights_8cpp_source.xhtml#l00028">CLConvertFullyConnectedWeights.cpp:28</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml_a97bd6c077f3c7769f575b82988b9b668"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">arm_compute::UniformQuantizationInfo::offset</a></div><div class="ttdeci">int32_t offset</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00062">QuantizationInfo.h:62</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_fully_connected_layer_reshape_weights_xhtml_a074e10cfb217e657b9e81adeca2abc68"><div class="ttname"><a href="classarm__compute_1_1_c_l_fully_connected_layer_reshape_weights.xhtml#a074e10cfb217e657b9e81adeca2abc68">arm_compute::CLFullyConnectedLayerReshapeWeights::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00067">CLFullyConnectedLayer.cpp:67</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="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a11851649b6a7cd12ae25cf72b769cfb9"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a11851649b6a7cd12ae25cf72b769cfb9">arm_compute::FullyConnectedLayerInfo::transpose_weights</a></div><div class="ttdeci">bool transpose_weights</div><div class="ttdoc">Transpose weights if true.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00796">Types.h:796</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_kernel_xhtml_ad5ba9d34a3a855bf1dd2e36316ff550a"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">arm_compute::ICLKernel::set_target</a></div><div class="ttdeci">void set_target(GPUTarget target)</div><div class="ttdoc">Set the targeted GPU architecture.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_kernel_8h_source.xhtml#l00271">ICLKernel.h:271</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< size_t >::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_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor::allocator()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00797">FullyConnectedLayerInfo::are_weights_reshaped</a>, <a class="el" href="_validate_8h_source.xhtml#l00543">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00327">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_asymm_helpers_8cpp_source.xhtml#l00035">arm_compute::quantization::calculate_quantized_multiplier_less_than_one()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00210">Dimensions< T >::cbegin()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00234">Dimensions< T >::cend()</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable< T >::clone()</a>, <a class="el" href="_c_l_convert_fully_connected_weights_8cpp_source.xhtml#l00028">CLConvertFullyConnectedWeights::configure()</a>, <a class="el" href="_c_l_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00079">CLGEMMMatrixAccumulateBiasesKernel::configure()</a>, <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00067">CLFullyConnectedLayerReshapeWeights::configure()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00047">CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">ITensorInfo::data_type()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler::get()</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#l00035">CLTensor::info()</a>, <a class="el" href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator::init()</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01030">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00045">Dimensions< size_t >::num_max_dimensions</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00062">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#l00293">TensorInfo::quantization_info()</a>, <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00798">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#l00061">UniformQuantizationInfo::scale</a>, <a class="el" href="_i_c_l_kernel_8h_source.xhtml#l00271">ICLKernel::set_target()</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#l00796">FullyConnectedLayerInfo::transpose_weights</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00134">QuantizationInfo::uniform()</a>, <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00249">CLFullyConnectedLayer::validate()</a>, <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">arm_compute::test::validation::weights</a>, and <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00795">FullyConnectedLayerInfo::weights_trained_layout</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_c_l_r_n_n_layer_8cpp_source.xhtml#l00069">CLRNNLayer::configure()</a>, <a class="el" href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00056">CLLSTMLayer::configure()</a>, and <a class="el" href="tests_2validation_2_c_l_2_fully_connected_layer_8cpp_source.xhtml#l00068">arm_compute::test::validation::DATA_TEST_CASE()</a>.</p> |
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| <a id="a1bee19103166ceb1eedef2e6a4fe6515"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a1bee19103166ceb1eedef2e6a4fe6515">◆ </a></span>operator=() <span class="overload">[1/2]</span></h2> |
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| <div class="memitem"> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
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| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
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| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
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| </div> |
| </div> |
| <a id="a8a5afe02cef06665b7d7349a8ecbd109"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a8a5afe02cef06665b7d7349a8ecbd109">◆ </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_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
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| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
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| <p>Default move assignment operator. </p> |
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| </div> |
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| <a id="aa9b93ef660fc3c5b4b19d3fc7b891b77"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">◆ </a></span>prepare()</h2> |
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| <td class="memname">void prepare </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
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| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
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| <p>Prepare the function for executing. </p> |
| <p>Any one off pre-processing step required by the function is handled here</p> |
| <dl class="section note"><dt>Note</dt><dd>Prepare stage might not need all the function's buffers' backing memory to be available in order to execute </dd></dl> |
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| <p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p> |
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| <p class="definition">Definition at line <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00383">383</a> of file <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml">CLFullyConnectedLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> {</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  {</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">auto</span> release_unused = [](<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml">CLTensor</a> * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>)</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  {</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">if</span>(!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>->is_used())</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">queue</a>().finish();</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>->allocator()->free();</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  }</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  };</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="comment">// Pointer to current weights</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *cur_weights = _original_weights;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">// Reshape of the weights if needed (happens only once)</span></div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  {</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="comment">// Run reshape weights kernel and mark weights as unused</span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  _reshape_weights_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  _reshape_weights_kernel.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  cur_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  cur_weights = &_reshape_weights_output;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  _are_weights_reshaped = <span class="keyword">true</span>;</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="comment">// Convert weights if needed (happens only once)</span></div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordflow">if</span>(!_are_weights_converted)</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  {</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  _converted_weights_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  _convert_weights.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  cur_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  _are_weights_converted = <span class="keyword">true</span>;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  }</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="comment">// Release reshaped weights if unused</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  release_unused(&_reshape_weights_output);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="comment">// Prepare GEMM prepare and release unused weights</span></div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordflow">if</span>(!_is_quantized)</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  {</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  }</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="comment">// Release converted weights if unused</span></div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  release_unused(&_reshape_weights_output);</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  release_unused(&_converted_weights_output);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> }</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< float > 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_c_l_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLGEMM::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="_c_l_g_e_m_m_8cpp_source.xhtml#l00632">CLGEMM.cpp:632</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</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#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ad381d1aed28b4b1e1f5a710633934580"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">arm_compute::CLScheduler::queue</a></div><div class="ttdeci">cl::CommandQueue & queue()</div><div class="ttdoc">Accessor for the associated CL command queue.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00102">CLScheduler.h:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_tensor.xhtml">arm_compute::ICLTensor</a></div><div class="ttdoc">Interface for OpenCL tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_tensor_8h_source.xhtml#l00042">ICLTensor.h:42</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml">arm_compute::CLTensor</a></div><div class="ttdoc">Basic implementation of the OpenCL tensor interface.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8h_source.xhtml#l00040">CLTensor.h:40</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator::allocate()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor::allocator()</a>, <a class="el" href="_error_8h_source.xhtml#l00337">ARM_COMPUTE_ERROR_ON</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler::get()</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="_c_l_g_e_m_m_8cpp_source.xhtml#l00632">CLGEMM::prepare()</a>, <a class="el" href="_c_l_scheduler_8h_source.xhtml#l00102">CLScheduler::queue()</a>, <a class="el" href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction::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> |
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| <p class="reference">Referenced by <a class="el" href="_c_l_r_n_n_layer_8cpp_source.xhtml#l00119">CLRNNLayer::prepare()</a>, and <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00347">CLFullyConnectedLayer::run()</a>.</p> |
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| <a id="ad1717410afd0be936c6213a63c8005fb"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">◆ </a></span>run()</h2> |
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| <td class="memname">void run </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</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_c_l_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl> |
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| <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="_c_l_fully_connected_layer_8cpp_source.xhtml#l00347">347</a> of file <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml">CLFullyConnectedLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> </div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="comment">// Linearize input if it comes from a convolutional layer</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  {</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  _flatten_layer.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  }</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="comment">// Run matrix multiply</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  {</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  }</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  {</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  }</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="comment">// Accumulate biases if provided</span></div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  _gemmlowp_output_stage.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  }</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  {</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">if</span>(_accumulate_biases)</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  {</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_accumulate_biases_kernel);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  }</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> }</div><div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMM::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="_c_l_g_e_m_m_8cpp_source.xhtml#l00572">CLGEMM.cpp:572</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMMLowpMatrixMultiplyCore::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="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00381">CLGEMMLowpMatrixMultiplyCore.cpp:381</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_fully_connected_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLFullyConnectedLayer::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="_c_l_fully_connected_layer_8cpp_source.xhtml#l00383">CLFullyConnectedLayer.cpp:383</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ae1a643e517f50bf0392fb6516dd7cf67"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">arm_compute::CLScheduler::enqueue</a></div><div class="ttdeci">void enqueue(ICLKernel &kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler.cpp:95</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00046">IMemoryGroup.h:46</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler::enqueue()</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler::get()</a>, <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00383">CLFullyConnectedLayer::prepare()</a>, <a class="el" href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction::run()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00381">CLGEMMLowpMatrixMultiplyCore::run()</a>, and <a class="el" href="_c_l_g_e_m_m_8cpp_source.xhtml#l00572">CLGEMM::run()</a>.</p> |
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| <p class="reference">Referenced by <a class="el" href="_c_l_r_n_n_layer_8cpp_source.xhtml#l00104">CLRNNLayer::run()</a>, and <a class="el" href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00619">CLLSTMLayer::run()</a>.</p> |
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| <h2 class="memtitle"><span class="permalink"><a href="#a8da875051f2d75a497fb2de9cdd2e6cb">◆ </a></span>validate()</h2> |
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| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> validate </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>input</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>weights</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>biases</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>output</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> </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> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">static</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_c_l_fully_connected_layer.xhtml">CLFullyConnectedLayer</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/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 info. Can be nullptr. Data type supported:Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[out]</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> |
| </ul> |
| </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">fc_info</td><td>(Optional) Fully connected layer additional info</td></tr> |
| </table> |
| </dd> |
| </dl> |
| <dl class="section return"><dt>Returns</dt><dd>a status </dd></dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00249">249</a> of file <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml">CLFullyConnectedLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> {</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</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="l00254"></a><span class="lineno"> 254</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <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>->num_dimensions() > 2);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordtype">bool</span> weights_reshaped = fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a11851649b6a7cd12ae25cf72b769cfb9">transpose_weights</a> ? fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a146a7be9d813ad80abb72a0bf6566cbc">are_weights_reshaped</a> : <span class="keyword">true</span>;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordtype">bool</span> is_fc_after_conv = <span class="keyword">true</span>;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a735ac6c2a02e320969625308810444f3">GPUTarget</a> gpu_target = <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a27561688e2fc60176608ef725a4ecb30">target</a>();</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &flatten_input = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->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>(input)).set_data_layout(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>));</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &reshaped_weights = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->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="l00264"></a><span class="lineno"> 264</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &converted_weights = weights_reshaped ? <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->set_is_resizable(<span class="keyword">true</span>).reset_padding()) : <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(*reshaped_weights.clone());</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &gemmlowp_output = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(output-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->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="l00266"></a><span class="lineno"> 266</span> </div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="comment">// Configure accumulate biases kernel for non quantized asymmetric types</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span> && !is_quantized)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, biases);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aacce47387e80c160a47584829ab0edb1">CLGEMMMatrixAccumulateBiasesKernel::validate</a>(output, biases, gpu_target));</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  }</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="comment">// With the Fully Connected layer we can have 4 different cases:</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="comment">// 1) Convolution layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// 2) Fully Connected layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="comment">// 3) Convolution layer -> Fully Connected layer with batches</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="comment">// 4) Fully Connected layer -> Fully Connected layer with batches</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_use = input;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *tmp_output = (is_quantized) ? &gemmlowp_output : output;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// Check if we have a fully connected layer with batches</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_batched_fc_layer = output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) > 1;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  {</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  is_fc_after_conv = (<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> >= 4) && (std::equal(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">cbegin</a>() + 3,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#afde273ebfa15fe83c690ad5cf6693c9f">cend</a>(),</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">cbegin</a>() + 1));</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  }</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  is_fc_after_conv = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  }</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">if</span>(!weights_reshaped)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  {</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="comment">// Validate reshape weights kernel</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_fully_connected_layer_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLFullyConnectedLayerReshapeWeights::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &reshaped_weights));</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  weights_to_use = &reshaped_weights;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  }</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">if</span>(is_fc_after_conv && (input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() != fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a89d0ed86875fd3fb395899187c84bc2f">weights_trained_layout</a>))</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="comment">// Validate convert weights kernel</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_convert_fully_connected_weights.xhtml#a01b81c1c60fd95dc360fd7ad680f114b">CLConvertFullyConnectedWeights::validate</a>(weights_to_use,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  &converted_weights,</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(),</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a89d0ed86875fd3fb395899187c84bc2f">weights_trained_layout</a>));</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  weights_to_use = &converted_weights;</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  }</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">if</span>(is_fc_after_conv)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  {</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="comment">// Fully Connected layer after a Convolution Layer without batches</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>((weights_to_use-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) != (input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2))));</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="comment">// Validate flatten kernel</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_flatten_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">CLFlattenLayer::validate</a>(input, &flatten_input));</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  input_to_use = &flatten_input;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  }</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  {</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="comment">// Fully Connected layer after a Fully Connected Layer without batches</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != weights_to_use-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="comment">// Validate matrix multiply kernel</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <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="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// Validate output stage for asymmetric quantized types</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> iq_info = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> wq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->quantization_info().uniform();</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> oq_info = output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = iq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> * wq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> / oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(multiplier);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(multiplier > 1.0f);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml#aee63e7671cf04d15be2da1b83d90e61b">CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate</a>(&gemmlowp_output, biases, output));</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  }</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> }</div><div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_flatten_layer_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_flatten_layer.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLFlattenLayer::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 CLFlattenLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_flatten_layer_8cpp_source.xhtml#l00040">CLFlattenLayer.cpp:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></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="classarm__compute_1_1_c_l_scheduler_xhtml_a27561688e2fc60176608ef725a4ecb30"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a27561688e2fc60176608ef725a4ecb30">arm_compute::CLScheduler::target</a></div><div class="ttdeci">GPUTarget target() const</div><div class="ttdoc">Get the target GPU.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00112">CLScheduler.h:112</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#l00193">Error.h:193</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div> |
| <div class="ttc" id="_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#l00791">Validate.h:791</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="classarm__compute_1_1_c_l_g_e_m_m_matrix_accumulate_biases_kernel_xhtml_aacce47387e80c160a47584829ab0edb1"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aacce47387e80c160a47584829ab0edb1">arm_compute::CLGEMMMatrixAccumulateBiasesKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *accum, const ITensorInfo *biases, GPUTarget gpu_target)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMMatrixAccumulateBi...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00106">CLGEMMMatrixAccumulateBiasesKernel.cpp:106</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml">arm_compute::UniformQuantizationInfo</a></div><div class="ttdoc">Quantization info when assuming per layer quantization.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00039">QuantizationInfo.h:39</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#l00061">QuantizationInfo.h:61</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></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#l00244">Error.h:244</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 &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#l00390">ShapeCalculator.h:390</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point_xhtml_aee63e7671cf04d15be2da1b83d90e61b"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml#aee63e7671cf04d15be2da1b83d90e61b">arm_compute::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min=0, int max=0)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMLowpQuantizeDownIn...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00056">CLGEMMLowpOutputStage.cpp:56</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#l00551">ShapeCalculator.h:551</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#l00160">Error.h:160</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</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</div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a146a7be9d813ad80abb72a0bf6566cbc"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a146a7be9d813ad80abb72a0bf6566cbc">arm_compute::FullyConnectedLayerInfo::are_weights_reshaped</a></div><div class="ttdeci">bool are_weights_reshaped</div><div class="ttdoc">Reshape the weights tensor if false.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00797">Types.h:797</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#l00134">QuantizationInfo.h:134</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a3f3e1a3200223e6a304a533b1016e749"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">arm_compute::ITensorInfo::quantization_info</a></div><div class="ttdeci">virtual QuantizationInfo quantization_info() const =0</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_convert_fully_connected_weights_xhtml_a01b81c1c60fd95dc360fd7ad680f114b"><div class="ttname"><a href="classarm__compute_1_1_c_l_convert_fully_connected_weights.xhtml#a01b81c1c60fd95dc360fd7ad680f114b">arm_compute::CLConvertFullyConnectedWeights::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const TensorShape &original_input_shape, DataLayout data_layout)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLConvertFullyConnectedW...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_convert_fully_connected_weights_8cpp_source.xhtml#l00036">CLConvertFullyConnectedWeights.cpp:36</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#l01030">Utils.h:1030</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_afde273ebfa15fe83c690ad5cf6693c9f"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#afde273ebfa15fe83c690ad5cf6693c9f">arm_compute::Dimensions::cend</a></div><div class="ttdeci">std::array< T, num_max_dimensions >::const_iterator cend() const</div><div class="ttdoc">Returns a read-only (constant) iterator that points one past the last element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00234">Dimensions.h:234</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_dimensions_xhtml_a8c9efb4e1ad142d58d65af400f20217d"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">arm_compute::Dimensions::cbegin</a></div><div class="ttdeci">std::array< T, num_max_dimensions >::const_iterator cbegin() const</div><div class="ttdoc">Returns a read-only (constant) iterator that points to the first element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00210">Dimensions.h:210</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a735ac6c2a02e320969625308810444f3"><div class="ttname"><a href="namespacearm__compute.xhtml#a735ac6c2a02e320969625308810444f3">arm_compute::GPUTarget</a></div><div class="ttdeci">GPUTarget</div><div class="ttdoc">Available GPU Targets.</div><div class="ttdef"><b>Definition:</b> <a href="_g_p_u_target_8h_source.xhtml#l00034">GPUTarget.h:34</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a89d0ed86875fd3fb395899187c84bc2f"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a89d0ed86875fd3fb395899187c84bc2f">arm_compute::FullyConnectedLayerInfo::weights_trained_layout</a></div><div class="ttdeci">DataLayout weights_trained_layout</div><div class="ttdoc">Layout that the weights have been trained with.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00795">Types.h:795</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="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a11851649b6a7cd12ae25cf72b769cfb9"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a11851649b6a7cd12ae25cf72b769cfb9">arm_compute::FullyConnectedLayerInfo::transpose_weights</a></div><div class="ttdeci">bool transpose_weights</div><div class="ttdoc">Transpose weights if true.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00796">Types.h:796</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</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< size_t >::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_c_l_fully_connected_layer_reshape_weights_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_c_l_fully_connected_layer_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::CLFullyConnectedLayerReshapeWeights::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 CLFullyConnectedLayerRes...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00074">CLFullyConnectedLayer.cpp:74</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00797">FullyConnectedLayerInfo::are_weights_reshaped</a>, <a class="el" href="_error_8h_source.xhtml#l00244">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00791">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#l00193">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="_error_8h_source.xhtml#l00160">ARM_COMPUTE_UNUSED</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00210">Dimensions< T >::cbegin()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00234">Dimensions< T >::cend()</a>, <a class="el" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">ICloneable< T >::clone()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00551">arm_compute::misc::shape_calculator::compute_flatten_shape()</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00390">arm_compute::misc::shape_calculator::compute_transposed_shape()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">ITensorInfo::data_layout()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">ITensorInfo::data_type()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler::get()</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01030">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::NCHW</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00045">Dimensions< size_t >::num_max_dimensions</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::QASYMM8</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#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00061">UniformQuantizationInfo::scale</a>, <a class="el" href="_c_l_scheduler_8h_source.xhtml#l00112">CLScheduler::target()</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#l00796">FullyConnectedLayerInfo::transpose_weights</a>, <a class="el" href="_quantization_info_8h_source.xhtml#l00134">QuantizationInfo::uniform()</a>, <a class="el" href="_c_l_convert_fully_connected_weights_8cpp_source.xhtml#l00036">CLConvertFullyConnectedWeights::validate()</a>, <a class="el" href="_c_l_flatten_layer_8cpp_source.xhtml#l00040">CLFlattenLayer::validate()</a>, <a class="el" href="_c_l_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00106">CLGEMMMatrixAccumulateBiasesKernel::validate()</a>, <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00074">CLFullyConnectedLayerReshapeWeights::validate()</a>, <a class="el" href="_c_l_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00056">CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate()</a>, <a class="el" href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">arm_compute::test::validation::weights</a>, and <a class="el" href="arm__compute_2core_2_types_8h_source.xhtml#l00795">FullyConnectedLayerInfo::weights_trained_layout</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml#l00140">CLFullyConnectedLayer::configure()</a>, <a class="el" href="_c_l_r_n_n_layer_8cpp_source.xhtml#l00044">CLRNNLayer::validate()</a>, and <a class="el" href="_c_l_l_s_t_m_layer_8cpp_source.xhtml#l00388">CLLSTMLayer::validate()</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following files:<ul> |
| <li>arm_compute/runtime/CL/functions/<a class="el" href="_c_l_fully_connected_layer_8h_source.xhtml">CLFullyConnectedLayer.h</a></li> |
| <li>src/runtime/CL/functions/<a class="el" href="_c_l_fully_connected_layer_8cpp_source.xhtml">CLFullyConnectedLayer.cpp</a></li> |
| </ul> |
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