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<div class="title">FullyConnected.cpp</div> </div>
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<a href="backends_2reference_2workloads_2_fully_connected_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_fully_connected_8hpp.xhtml">FullyConnected.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_ref_workload_utils_8hpp.xhtml">RefWorkloadUtils.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> {</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a5739b990434e95ee13ff6d99824ffaf3"> 13</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a5739b990434e95ee13ff6d99824ffaf3">FullyConnected</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& rInputShape,</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& rInputDecoder,</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& rOutputShape,</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <a class="code" href="classarmnn_1_1_encoder.xhtml">Encoder<float></a>& rOutputEncoder,</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& rWeightsShape,</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& rWeightDecoder,</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <a class="code" href="classarmnn_1_1_decoder.xhtml">Decoder<float></a>& rBiasDecoder,</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> K,</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> transposeWeights)</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="comment">// Perform FullyConnected implementation</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = rOutputShape[1];</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> std::vector<float> decodedInputs = rInputDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#a6f1a1177c9e21355a0c3875e5f280c8a">DecodeTensor</a>(rInputShape);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> std::vector<float> decodedWeights = rWeightDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#a6f1a1177c9e21355a0c3875e5f280c8a">DecodeTensor</a>(rWeightsShape);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> biasShape{outputSize};</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">const</span> std::vector<float> decodedBiases = biasEnabled ? rBiasDecoder.<a class="code" href="classarmnn_1_1_decoder.xhtml#a6f1a1177c9e21355a0c3875e5f280c8a">DecodeTensor</a>(biasShape) : std::vector<float>();</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n = 0; n < rInputShape[0]; n++)</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelOutput = 0; channelOutput < outputSize; channelOutput++)</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordtype">float</span> outval = 0.f;</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelInput = 0; channelInput < K; channelInput++)</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordtype">float</span> weight;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordflow">if</span> (transposeWeights)</div><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>  weight = decodedWeights[channelOutput * K + channelInput];</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="keywordflow">else</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  weight = decodedWeights[channelInput * outputSize + channelOutput];</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  }</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  outval += weight * decodedInputs[n * K + channelInput];</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  }</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  {</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  outval += decodedBiases[channelOutput];</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</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>  rOutputEncoder[n * outputSize + channelOutput];</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  rOutputEncoder.<a class="code" href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">Set</a>(outval);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</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> }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> } <span class="comment">//namespace armnn</span></div><div class="ttc" id="_ref_workload_utils_8hpp_xhtml"><div class="ttname"><a href="_ref_workload_utils_8hpp.xhtml">RefWorkloadUtils.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_encoder_xhtml_ae3b62b846a9c239f332830b9e36030eb"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml#ae3b62b846a9c239f332830b9e36030eb">armnn::Encoder::Set</a></div><div class="ttdeci">virtual void Set(IType right)=0</div></div>
<div class="ttc" id="classarmnn_1_1_encoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_encoder.xhtml">armnn::Encoder< float ></a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a5739b990434e95ee13ff6d99824ffaf3"><div class="ttname"><a href="namespacearmnn.xhtml#a5739b990434e95ee13ff6d99824ffaf3">armnn::FullyConnected</a></div><div class="ttdeci">void FullyConnected(const TensorShape &rInputShape, Decoder< float > &rInputDecoder, const TensorShape &rOutputShape, Encoder< float > &rOutputEncoder, const TensorShape &rWeightsShape, Decoder< float > &rWeightDecoder, Decoder< float > &rBiasDecoder, const bool biasEnabled, const unsigned int K, const bool transposeWeights)</div><div class="ttdoc">Performs a matrix multiplication and optionally adds a bias. </div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_fully_connected_8cpp_source.xhtml#l00013">FullyConnected.cpp:13</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_xhtml_a6f1a1177c9e21355a0c3875e5f280c8a"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml#a6f1a1177c9e21355a0c3875e5f280c8a">armnn::Decoder::DecodeTensor</a></div><div class="ttdeci">virtual std::vector< float > DecodeTensor(const TensorShape &tensorShape, const unsigned int channelMultiplier=1, bool isDepthwise=false)=0</div></div>
<div class="ttc" id="_fully_connected_8hpp_xhtml"><div class="ttname"><a href="_fully_connected_8hpp.xhtml">FullyConnected.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_decoder_xhtml"><div class="ttname"><a href="classarmnn_1_1_decoder.xhtml">armnn::Decoder< float ></a></div></div>
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