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| <a href="#pub-methods">Public Member Functions</a> | |
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| <div class="title">NERNNLayer Class Reference</div> </div> |
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| <div class="contents"> |
| |
| <p>Basic function to run <a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a>. |
| <a href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_n_e_r_n_n_layer_8h_source.xhtml">NERNNLayer.h</a>></code></p> |
| <div class="dynheader"> |
| Collaboration diagram for NERNNLayer:</div> |
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| <table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> |
| Public Member Functions</h2></td></tr> |
| <tr class="memitem:a22580824d9d7f1d67c3db8ac601113b3"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#a22580824d9d7f1d67c3db8ac601113b3">NERNNLayer</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:a22580824d9d7f1d67c3db8ac601113b3"><td class="mdescLeft"> </td><td class="mdescRight">Default constructor. <a href="#a22580824d9d7f1d67c3db8ac601113b3">More...</a><br /></td></tr> |
| <tr class="separator:a22580824d9d7f1d67c3db8ac601113b3"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ab6231543c4071f67f7acd1b868017185"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#ab6231543c4071f67f7acd1b868017185">NERNNLayer</a> (const <a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:ab6231543c4071f67f7acd1b868017185"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#ab6231543c4071f67f7acd1b868017185">More...</a><br /></td></tr> |
| <tr class="separator:ab6231543c4071f67f7acd1b868017185"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ad71b9e0c36eb029efea21034a525797d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#ad71b9e0c36eb029efea21034a525797d">NERNNLayer</a> (<a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:ad71b9e0c36eb029efea21034a525797d"><td class="mdescLeft"> </td><td class="mdescRight">Default move constructor. <a href="#ad71b9e0c36eb029efea21034a525797d">More...</a><br /></td></tr> |
| <tr class="separator:ad71b9e0c36eb029efea21034a525797d"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ae2d47df9fdf935549764571144c6c1c1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#ae2d47df9fdf935549764571144c6c1c1">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:ae2d47df9fdf935549764571144c6c1c1"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#ae2d47df9fdf935549764571144c6c1c1">More...</a><br /></td></tr> |
| <tr class="separator:ae2d47df9fdf935549764571144c6c1c1"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a9b76a4c1bbcb9d6dc0573fb8ef3be3b8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#a9b76a4c1bbcb9d6dc0573fb8ef3be3b8">operator=</a> (<a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a9b76a4c1bbcb9d6dc0573fb8ef3be3b8"><td class="mdescLeft"> </td><td class="mdescRight">Default move assignment operator. <a href="#a9b76a4c1bbcb9d6dc0573fb8ef3be3b8">More...</a><br /></td></tr> |
| <tr class="separator:a9b76a4c1bbcb9d6dc0573fb8ef3be3b8"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a23ed39a3f37b8b100cec84427d55f9cf"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#a23ed39a3f37b8b100cec84427d55f9cf">configure</a> (const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *recurrent_weights, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *bias, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *hidden_state, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &info)</td></tr> |
| <tr class="memdesc:a23ed39a3f37b8b100cec84427d55f9cf"><td class="mdescLeft"> </td><td class="mdescRight">Initialize the function. <a href="#a23ed39a3f37b8b100cec84427d55f9cf">More...</a><br /></td></tr> |
| <tr class="separator:a23ed39a3f37b8b100cec84427d55f9cf"><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_n_e_r_n_n_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_n_e_r_n_n_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:a25cb8dc27e1dcd3cf00e930ae01ba97e"><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_n_e_r_n_n_layer.xhtml#a25cb8dc27e1dcd3cf00e930ae01ba97e">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> *recurrent_weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *bias, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *hidden_state, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, const <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &info)</td></tr> |
| <tr class="memdesc:a25cb8dc27e1dcd3cf00e930ae01ba97e"><td class="mdescLeft"> </td><td class="mdescRight">Initialize the function. <a href="#a25cb8dc27e1dcd3cf00e930ae01ba97e">More...</a><br /></td></tr> |
| <tr class="separator:a25cb8dc27e1dcd3cf00e930ae01ba97e"><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 run <a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a>. </p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_r_n_n_layer_8h_source.xhtml#l00041">41</a> of file <a class="el" href="_n_e_r_n_n_layer_8h_source.xhtml">NERNNLayer.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="a22580824d9d7f1d67c3db8ac601113b3"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a22580824d9d7f1d67c3db8ac601113b3">◆ </a></span>NERNNLayer() <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_n_e_r_n_n_layer.xhtml">NERNNLayer</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>Default constructor. </p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00036">36</a> of file <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml">NERNNLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation_kernel(), _fully_connected(memory_manager), _copy_kernel(), _fully_connected_out(), _gemm_output(),</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  _add_output(), _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> }</div></div><!-- fragment --> |
| </div> |
| </div> |
| <a id="ab6231543c4071f67f7acd1b868017185"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ab6231543c4071f67f7acd1b868017185">◆ </a></span>NERNNLayer() <span class="overload">[2/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</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> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
| |
| </div> |
| </div> |
| <a id="ad71b9e0c36eb029efea21034a525797d"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ad71b9e0c36eb029efea21034a525797d">◆ </a></span>NERNNLayer() <span class="overload">[3/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</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> |
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| </div><div class="memdoc"> |
| |
| <p>Default move constructor. </p> |
| |
| </div> |
| </div> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a id="a23ed39a3f37b8b100cec84427d55f9cf"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a23ed39a3f37b8b100cec84427d55f9cf">◆ </a></span>configure()</h2> |
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| <td class="memname">void configure </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </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.xhtml">ITensor</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.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>recurrent_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.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>bias</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>hidden_state</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>output</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> & </td> |
| <td class="paramname"><em>info</em> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Initialize the function. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Input is a 2-D tensor of shape [input_size, batch_size]. Data types supported: F16/F32 </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor of shape [input_size, num_units] that multiplies the input. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_weights</td><td>Weights tensor of shape [num_units, num_units] that multiplies the current 'state'. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">bias</td><td>Bias vector of shape [num_units]. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Output tensor of shape [num_units, batch_size]. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in,out]</td><td class="paramname">hidden_state</td><td>Output tensor of shape [num_units, batch_size]. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">info</td><td>Activation layer parameter. </td></tr> |
| </table> |
| </dd> |
| </dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00067">67</a> of file <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml">NERNNLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, recurrent_weights, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, hidden_state, output);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#a25cb8dc27e1dcd3cf00e930ae01ba97e">NERNNLayer::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->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>(), recurrent_weights->info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), hidden_state->info(), output->info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>));</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b">misc::shape_calculator::compute_rnn_shape</a>(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="comment">// Manage intermediate buffers and configure</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  _fully_connected_out.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">// Manage intermediate buffers and configure</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_fully_connected_out);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  _fully_connected.<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a91fb7694ae938cfec69ff6474451de49">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, &_fully_connected_out);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_gemm_output);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  _gemm_state_f.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">configure</a>(hidden_state, recurrent_weights, <span class="keyword">nullptr</span>, &_gemm_output, 1.f, 0.f);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  _add_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()));</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_add_output);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  _add_kernel.<a class="code" href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#ae549ed675eab6d763ac6ffd18d226c27">configure</a>(&_fully_connected_out, &_gemm_output, &_add_output, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  _fully_connected_out.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  _activation_kernel.<a class="code" href="classarm__compute_1_1_n_e_activation_layer_kernel.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">configure</a>(&_add_output, hidden_state, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  _add_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  _copy_kernel.<a class="code" href="classarm__compute_1_1_n_e_copy_kernel.xhtml#ab914edcfa864626e92184b2b0ec23c30">configure</a>(hidden_state, output);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> }</div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">AbsoluteDifference.cpp:97</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#l00041">CLTensor.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_activation_layer_kernel_xhtml_adfb5ef37594fc9371c4a2b95e3d5e31b"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer_kernel.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">arm_compute::NEActivationLayerKernel::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, ActivationLayerInfo activation_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_kernel_8cpp_source.xhtml#l00121">NEActivationLayerKernel.cpp:121</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_arithmetic_addition_kernel_xhtml_ae549ed675eab6d763ac6ffd18d226c27"><div class="ttname"><a href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#ae549ed675eab6d763ac6ffd18d226c27">arm_compute::NEArithmeticAdditionKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy)</div><div class="ttdoc">Initialise the kernel's input, output and border mode.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00915">NEArithmeticAdditionKernel.cpp:915</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_af98bc3ef5c65dbb63bc79700ccdd043b"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b">arm_compute::misc::shape_calculator::compute_rnn_shape</a></div><div class="ttdeci">TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned int batch_size)</div><div class="ttdoc">Calculate the RNN shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00841">ShapeCalculator.h:841</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_a91fb7694ae938cfec69ff6474451de49"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a91fb7694ae938cfec69ff6474451de49">arm_compute::NEFullyConnectedLayer::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00141">NEFullyConnectedLayer.cpp:141</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3a77be8aebd8e00522b32061d46ccdbd"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">arm_compute::test::validation::bias</a></div><div class="ttdeci">CLTensor bias</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00189">ConvolutionLayer.cpp:189</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_copy_kernel_xhtml_ab914edcfa864626e92184b2b0ec23c30"><div class="ttname"><a href="classarm__compute_1_1_n_e_copy_kernel.xhtml#ab914edcfa864626e92184b2b0ec23c30">arm_compute::NECopyKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const PaddingList &padding=PaddingList())</div><div class="ttdoc">Initialize the kernel's input, output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_copy_kernel_8cpp_source.xhtml#l00078">NECopyKernel.cpp:78</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_a385241dcc5062af6ecac8bdafe01bb2a"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">arm_compute::NEGEMM::configure</a></div><div class="ttdeci">void configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00051">NEGEMM.cpp:51</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_r_n_n_layer_xhtml_a25cb8dc27e1dcd3cf00e930ae01ba97e"><div class="ttname"><a href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#a25cb8dc27e1dcd3cf00e930ae01ba97e">arm_compute::NERNNLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *recurrent_weights, const ITensorInfo *bias, const ITensorInfo *hidden_state, const ITensorInfo *output, const ActivationLayerInfo &info)</div><div class="ttdoc">Initialize the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00042">NERNNLayer.cpp:42</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86"><div class="ttname"><a href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::ConvertPolicy::SATURATE</a></div><div class="ttdoc">Saturate.</div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00455">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00189">arm_compute::test::validation::bias</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00841">arm_compute::misc::shape_calculator::compute_rnn_shape()</a>, <a class="el" href="_n_e_copy_kernel_8cpp_source.xhtml#l00078">NECopyKernel::configure()</a>, <a class="el" href="_n_e_activation_layer_kernel_8cpp_source.xhtml#l00121">NEActivationLayerKernel::configure()</a>, <a class="el" href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00915">NEArithmeticAdditionKernel::configure()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00051">NEGEMM::configure()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00141">NEFullyConnectedLayer::configure()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor::info()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::info</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator::init()</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::SATURATE</a>, <a class="el" href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">arm_compute::test::validation::shape</a>, <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00042">NERNNLayer::validate()</a>, and <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>.</p> |
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| <a id="ae2d47df9fdf935549764571144c6c1c1"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ae2d47df9fdf935549764571144c6c1c1">◆ </a></span>operator=() <span class="overload">[1/2]</span></h2> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
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| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
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| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
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| <a id="a9b76a4c1bbcb9d6dc0573fb8ef3be3b8"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a9b76a4c1bbcb9d6dc0573fb8ef3be3b8">◆ </a></span>operator=() <span class="overload">[2/2]</span></h2> |
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| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml">NERNNLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
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| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
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| <p>Default move assignment operator. </p> |
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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"> |
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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="_n_e_r_n_n_layer_8cpp_source.xhtml#l00120">120</a> of file <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml">NERNNLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  _fully_connected.<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  _gemm_state_f.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  }</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> }</div><div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMM::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM.cpp:335</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEFullyConnectedLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00417">NEFullyConnectedLayer.cpp:417</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM::prepare()</a>, and <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00417">NEFullyConnectedLayer::prepare()</a>.</p> |
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| <p class="reference">Referenced by <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00103">NERNNLayer::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> |
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| <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_n_e_r_n_n_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> |
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| <p class="definition">Definition at line <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00103">103</a> of file <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml">NERNNLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  _fully_connected.<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  _gemm_state_f.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_add_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_activation_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// copy hidden out to output</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_copy_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> }</div><div class="ttc" id="classarm__compute_1_1_n_e_r_n_n_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_r_n_n_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NERNNLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00120">NERNNLayer.cpp:120</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMM::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM.cpp:285</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEFullyConnectedLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00381">NEFullyConnectedLayer.cpp:381</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00120">NERNNLayer::prepare()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM::run()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00381">NEFullyConnectedLayer::run()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p> |
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| </div> |
| </div> |
| <a id="a25cb8dc27e1dcd3cf00e930ae01ba97e"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a25cb8dc27e1dcd3cf00e930ae01ba97e">◆ </a></span>validate()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <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>recurrent_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>bias</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>hidden_state</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">const <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> & </td> |
| <td class="paramname"><em>info</em> </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>Initialize the function. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Input is a 2-D tensor of shape [input_size, batch_size]. Data types supported: F16/F32 </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor of shape [input_size, num_units] that multiplies the input. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">recurrent_weights</td><td>Weights tensor of shape [num_units, num_units] that multiplies the current 'state'. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">bias</td><td>Bias vector of shape [num_units]. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">output</td><td>Output tensor of shape [num_units, batch_size]. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">hidden_state</td><td>Output tensor of shape [num_units, batch_size]. Data types supported: Same as <code>input</code> </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">info</td><td>Activation layer parameter.</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="_n_e_r_n_n_layer_8cpp_source.xhtml#l00042">42</a> of file <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml">NERNNLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, recurrent_weights, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, hidden_state, output);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</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#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_width) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_width));</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</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>->dimension(idx_height) != recurrent_weights->dimension(idx_width));</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(recurrent_weights->dimension(idx_width) != recurrent_weights->dimension(idx_height));</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</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#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>->num_dimensions() != 1);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</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#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>->dimension(idx_width) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_height));</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(hidden_state->dimension(idx_width) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_height));</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(hidden_state->dimension(idx_height) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_height));</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output->tensor_shape(), hidden_state->tensor_shape());</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">auto</span> shape_info = TensorInfo(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b">misc::shape_calculator::compute_rnn_shape</a>(recurrent_weights, hidden_state->dimension(idx_height)), 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_type());</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">NEFullyConnectedLayer::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, &shape_info));</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#a5e951bf3e414ddcd908245bcf284b08f">NEArithmeticAdditionKernel::validate</a>(&shape_info, &shape_info, &shape_info, <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">ConvertPolicy::SATURATE</a>));</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayerKernel::validate</a>(&shape_info, &shape_info, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>));</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> }</div><div class="ttc" id="classarm__compute_1_1_n_e_arithmetic_addition_kernel_xhtml_a5e951bf3e414ddcd908245bcf284b08f"><div class="ttname"><a href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml#a5e951bf3e414ddcd908245bcf284b08f">arm_compute::NEArithmeticAdditionKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEArithmeticAdditionKern...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00971">NEArithmeticAdditionKernel.cpp:971</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00204">Error.h:204</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_af98bc3ef5c65dbb63bc79700ccdd043b"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b">arm_compute::misc::shape_calculator::compute_rnn_shape</a></div><div class="ttdeci">TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned int batch_size)</div><div class="ttdoc">Calculate the RNN shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00841">ShapeCalculator.h:841</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#l00296">Error.h:296</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a1da797d2762c1cdbb73bfc83136c3a38"><div class="ttname"><a href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00288">Validate.h:288</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3a77be8aebd8e00522b32061d46ccdbd"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">arm_compute::test::validation::bias</a></div><div class="ttdeci">CLTensor bias</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00189">ConvolutionLayer.cpp:189</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_activation_layer_kernel_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer_kernel.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::NEActivationLayerKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEActivationLayerKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_kernel_8cpp_source.xhtml#l00778">NEActivationLayerKernel.cpp:778</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_a8da875051f2d75a497fb2de9cdd2e6cb"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">arm_compute::NEFullyConnectedLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEFullyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00275">NEFullyConnectedLayer.cpp:275</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86"><div class="ttname"><a href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::ConvertPolicy::SATURATE</a></div><div class="ttdoc">Saturate.</div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_error_8h_source.xhtml#l00296">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00288">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>, <a class="el" href="_validate_8h_source.xhtml#l00163">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00189">arm_compute::test::validation::bias</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00841">arm_compute::misc::shape_calculator::compute_rnn_shape()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::info</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6fa4729d95f983955f0d93a30179deb2b86">arm_compute::SATURATE</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_n_e_activation_layer_kernel_8cpp_source.xhtml#l00778">NEActivationLayerKernel::validate()</a>, <a class="el" href="_n_e_arithmetic_addition_kernel_8cpp_source.xhtml#l00971">NEArithmeticAdditionKernel::validate()</a>, <a class="el" href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00275">NEFullyConnectedLayer::validate()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml#l00067">NERNNLayer::configure()</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following files:<ul> |
| <li>arm_compute/runtime/NEON/functions/<a class="el" href="_n_e_r_n_n_layer_8h_source.xhtml">NERNNLayer.h</a></li> |
| <li>src/runtime/NEON/functions/<a class="el" href="_n_e_r_n_n_layer_8cpp_source.xhtml">NERNNLayer.cpp</a></li> |
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