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| <a href="_n_e_fully_connected_layer_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 (c) 2017-2020 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_n_e_fully_connected_layer_8h.xhtml">arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_size2_d_8h.xhtml">arm_compute/core/Size2D.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_validate_8h.xhtml">arm_compute/core/Validate.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_shape_calculator_8h.xhtml">arm_compute/core/utils/misc/ShapeCalculator.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_asymm_helpers_8h.xhtml">arm_compute/core/utils/quantization/AsymmHelpers.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_n_e_scheduler_8h.xhtml">arm_compute/runtime/NEON/NEScheduler.h</a>"</span></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> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include <cmath></span></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="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></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="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml">arm_compute::misc::shape_calculator</a>;</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="keyword">namespace</span></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> Status validate_mm(<span class="keyword">const</span> ITensorInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> ITensorInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo &output)</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>.data_type()))</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// Extract and negate input and weights offset</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">const</span> QuantizationInfo input_quantization_info(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>.quantization_info().uniform().scale, -<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>.quantization_info().uniform().offset);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">const</span> QuantizationInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#afff44a0110c3767f144c9cb8e6f81625">weights_quantization_info</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.quantization_info().uniform().scale, -<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.quantization_info().uniform().offset);</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>  <span class="comment">// Validate gemmlowp function</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</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_g_e_m_m_lowp_matrix_multiply_core.xhtml#a8c3cf2d65afb288e39909171ada19566">NEGEMMLowpMatrixMultiplyCore::validate</a>(&<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>.clone()->set_quantization_info(input_quantization_info),</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.clone()->set_quantization_info(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#afff44a0110c3767f144c9cb8e6f81625">weights_quantization_info</a>),</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  &output));</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>  <span class="keywordflow">else</span></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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">NEGEMM::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>, <span class="keyword">nullptr</span>, &output, 1.f, 0.0f, GEMMInfo(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">true</span> <span class="comment">/* Reshape weights only for the first run */</span>)));</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">return</span> Status{};</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="comment">// namespace</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"><a class="line" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a83a344e60eb7db895953a942abf16628"> 66</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a83a344e60eb7db895953a942abf16628">NEFullyConnectedLayerReshapeWeights::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">auto</span> k = arm_compute::support::cpp14::make_unique<NETransposeKernel>();</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  k->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  _kernel = std::move(k);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> }</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883"> 73</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEFullyConnectedLayerReshapeWeights::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output)</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> {</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_n_e_transpose_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NETransposeKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</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"><a class="line" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a9845f83f6f3bc45f9fb57ea1345e3dd3"> 78</a></span> <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a9845f83f6f3bc45f9fb57ea1345e3dd3">NEFullyConnectedLayer::NEFullyConnectedLayer</a>(std::shared_ptr<IMemoryManager> memory_manager, <a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> *weights_manager)</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  : _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten_kernel(), _convert_weights(), _convert_weights_managed(), _reshape_weights_function(),</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  _reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(),</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _accumulate_biases(false),</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  _is_quantized(false), _is_prepared(false)</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 class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="keywordtype">void</span> NEFullyConnectedLayer::configure_mm(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="comment">// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// Extract and negate input and weights offset</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> input_quantization_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info();</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#afff44a0110c3767f144c9cb8e6f81625">weights_quantization_info</a> = <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>();</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->set_quantization_info(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(input_quantization_info.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>, -input_quantization_info.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</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>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#afff44a0110c3767f144c9cb8e6f81625">weights_quantization_info</a>.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>, -<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#afff44a0110c3767f144c9cb8e6f81625">weights_quantization_info</a>.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// Configure gemmlowp function</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ae939cbc6a8a6747f193bfe8b54a7881c">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>, <span class="keyword">nullptr</span>, output);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="comment">// Revert back QuantizatioInfo as input and weights could be used in other fully connected layers</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->set_quantization_info(input_quantization_info);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</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>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#afff44a0110c3767f144c9cb8e6f81625">weights_quantization_info</a>);</div><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>  <span class="keywordflow">else</span></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>  <span class="comment">// Configure matrix multiply kernel</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">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>, <span class="keyword">nullptr</span>, output, 1.f, 0.0f, GEMMInfo(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">true</span> <span class="comment">/* Reshape weights only for the first run */</span>));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</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> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="keywordtype">void</span> NEFullyConnectedLayer::configure_conv_fc(<span class="keyword">const</span> ITensor *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> ITensor *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, ITensor *output)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((<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#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1) != (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(0) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(1) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(2))));</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">// If the fully connected layer is called after a convolution layer, the input tensor must be linearized</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// Initialize output tensor for flatten</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  TensorShape shape_flatten = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75">compute_flatten_shape</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info());</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  _flatten_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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->clone()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(shape_flatten));</div><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="comment">// Configure flatten kernel</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_flatten_output);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  _flatten_kernel.<a class="code" href="classarm__compute_1_1_n_e_flatten_layer_kernel.xhtml#a83a344e60eb7db895953a942abf16628">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &_flatten_output);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// Configure matrix multiply kernel</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  configure_mm(&_flatten_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</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>  <span class="comment">// Allocate the output tensor for flatten once all the configure methods have been called</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  _flatten_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="l00131"></a><span class="lineno"> 131</span> }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="keywordtype">void</span> NEFullyConnectedLayer::configure_fc_fc(<span class="keyword">const</span> ITensor *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> ITensor *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, ITensor *output)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(0) != <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#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1));</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Configure matrix multiply kernel</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  configure_mm(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a91fb7694ae938cfec69ff6474451de49"> 141</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a91fb7694ae938cfec69ff6474451de49">NEFullyConnectedLayer::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> fc_info)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> {</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// Perform validate step</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</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>, output);</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_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">NEFullyConnectedLayer::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(),</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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type());</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">if</span>(_weights_manager)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  {</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a18bd2d54155972d4d740fbba8337a27e">manage</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  }</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="comment">// Configure gemmlowp output</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  _gemmlowp_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>(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="l00168"></a><span class="lineno"> 168</span>  }</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// Configure accumulate biases kernel for non quantized asymmetric types</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span> && !_is_quantized)</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>  _accumulate_biases = <span class="keyword">true</span>;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="comment">// Configure accumulate biases kernel</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  _accumulate_biases_kernel.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aed1adf983092c2f8af29eba1dc29920c">configure</a>(output, biases);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// With the Fully Connected layer we can have 4 different cases:</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// 1) Convolution layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// 2) Fully Connected layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// 3) Convolution layer -> Fully Connected layer with batches</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// 4) Fully Connected layer -> Fully Connected layer with batches</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="comment">// Check if we have a fully connected layer with batches</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <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="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  _is_fc_after_conv = (<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> >= 4) && (std::equal(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape().cbegin() + 3,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape().cend(),</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</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="l00194"></a><span class="lineno"> 194</span>  }</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  _is_fc_after_conv = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->num_dimensions() > 1;</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> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="comment">// Reshape weights if needed</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>))</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  _reshape_weights_managed_function.<a class="code" href="classarm__compute_1_1weights__transformations_1_1_n_e_fully_connected_layer_reshape_weights_managed.xhtml#afad51fa14022cfbe5a27548024002aa2">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  weights_to_use = _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">acquire</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_reshape_weights_managed_function);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  }</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">else</span></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">// Reshape the weights</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  _reshape_weights_function.<a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a83a344e60eb7db895953a942abf16628">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_reshape_weights_output);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  weights_to_use = &_reshape_weights_output;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  }</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>  <span class="comment">// Convert weights if needed</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">if</span>(_is_fc_after_conv && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout() != 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="l00218"></a><span class="lineno"> 218</span>  {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(weights_to_use))</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  _convert_weights_managed.<a class="code" href="classarm__compute_1_1weights__transformations_1_1_n_e_convert_fully_connected_weights_managed.xhtml#a4db9a257c48616afe7fdd7e445fad8fe">configure</a>(weights_to_use,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape(),</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</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="l00224"></a><span class="lineno"> 224</span>  weights_to_use = _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">acquire</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_convert_weights_managed);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="comment">// Convert weights</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  _convert_weights.<a class="code" href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ac0cfd5b64b24f3dfae04ee3d0f258624">configure</a>(weights_to_use,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  &_converted_weights_output,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape(),</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</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="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  weights_to_use = &_converted_weights_output;</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>  _are_weights_converted = <span class="keyword">false</span>;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  }</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *tmp_output = (_is_quantized) ? &_gemmlowp_output : output;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  {</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="comment">// Fully Connected layer after a Convolution Layer without batches</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  configure_conv_fc(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, weights_to_use, tmp_output);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  }</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">else</span></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>  <span class="comment">// Fully Connected layer after a Fully Connected Layer without batches</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  configure_fc_fc(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, weights_to_use, tmp_output);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="comment">// Configure output stage for asymmetric quantized types</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> iq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info().uniform();</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</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="l00256"></a><span class="lineno"> 256</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="l00257"></a><span class="lineno"> 257</span> </div><div class="line"><a name="l00258"></a><span class="lineno"> 258</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="l00259"></a><span class="lineno"> 259</span>  int32_t output_multiplier;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  int32_t output_shift;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a63fdf412c27b0151bd4495c64cc112da">quantization::calculate_quantized_multiplier</a>(multiplier, &output_multiplier, &output_shift);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">GEMMLowpOutputStageInfo</a> gemmlowp_output_stage_info;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a1cfb92f1c287bf099c3fca0ef0391a2b">gemmlowp_multiplier</a> = output_multiplier;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a3f0613aeb69c326e7d8ffb34b44fae94">gemmlowp_shift</a> = output_shift;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a01934c5087f5193aaf3ea9bf41d1a8dc">gemmlowp_offset</a> = oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a6e019ad85979fd73c74f97e5483faf35">type</a> = <a class="code" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a>;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  _gemmlowp_output_stage.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a215ad3877ff40dcc4e6e39f25f2a27f0">configure</a>(&_gemmlowp_output, biases, output, gemmlowp_output_stage_info);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  _gemmlowp_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="l00270"></a><span class="lineno"> 270</span>  }</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  _are_weights_reshaped = _are_weights_reshaped || 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="l00273"></a><span class="lineno"> 273</span> }</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb"> 275</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a8da875051f2d75a497fb2de9cdd2e6cb">NEFullyConnectedLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output,</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> fc_info)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> {</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(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="l00279"></a><span class="lineno"> 279</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>, output);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">DataType::QASYMM8_SIGNED</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <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="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</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="l00285"></a><span class="lineno"> 285</span>  <span class="keywordtype">bool</span> is_fc_after_conv = <span class="keyword">true</span>;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_type());</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>  <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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</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#a83efb6708574e67d13965bcd2059ad75">compute_flatten_shape</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>)));</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <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="l00290"></a><span class="lineno"> 290</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="l00291"></a><span class="lineno"> 291</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="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="comment">// Configure accumulate biases kernel for non quantized asymmetric types</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span> && !is_quantized)</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>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, biases);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#a67759131249ecc51533fe269d0d90c51">NEGEMMMatrixAccumulateBiasesKernel::validate</a>(output, biases));</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="comment">// With the Fully Connected layer we can have 4 different cases:</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="comment">// 1) Convolution layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// 2) Fully Connected layer -> Fully Connected layer without batches</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="comment">// 3) Convolution layer -> Fully Connected layer with batches</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="comment">// 4) Fully Connected layer -> Fully Connected layer with batches</span></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="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <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="l00308"></a><span class="lineno"> 308</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="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="comment">// Check if we have a fully connected layer with batches</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <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="l00312"></a><span class="lineno"> 312</span> </div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  is_fc_after_conv = (<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> >= 4) && (std::equal(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape().cbegin() + 3,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape().cend(),</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</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="l00318"></a><span class="lineno"> 318</span>  }</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  {</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  is_fc_after_conv = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->num_dimensions() > 1;</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> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">if</span>(!weights_reshaped)</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  {</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="comment">// Validate reshape weights kernel</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</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_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEFullyConnectedLayerReshapeWeights::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &reshaped_weights));</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  weights_to_use = &reshaped_weights;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  }</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="keywordflow">if</span>(is_fc_after_conv && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout() != 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="l00332"></a><span class="lineno"> 332</span>  {</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="comment">// Validate convert weights kernel</span></div><div class="line"><a name="l00334"></a><span class="lineno"> 334</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_convert_fully_connected_weights.xhtml#a01b81c1c60fd95dc360fd7ad680f114b">NEConvertFullyConnectedWeights::validate</a>(weights_to_use,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  &converted_weights,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape(),</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</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="l00338"></a><span class="lineno"> 338</span>  weights_to_use = &converted_weights;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  }</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">if</span>(is_fc_after_conv)</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>  <span class="comment">// Fully Connected layer after a Convolution Layer without batches</span></div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <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) != (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(0) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(1) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(2))));</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="comment">// Validate flatten kernel</span></div><div class="line"><a name="l00347"></a><span class="lineno"> 347</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_flatten_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEFlattenLayerKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &flatten_input));</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  input_to_use = &flatten_input;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  }</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="comment">// Fully Connected layer after a Fully Connected Layer without batches</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <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(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="l00354"></a><span class="lineno"> 354</span>  }</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="comment">// Validate matrix multiply kernel</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</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="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="comment">// Validate output stage for asymmetric quantized types</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> iq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->quantization_info().uniform();</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</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="l00363"></a><span class="lineno"> 363</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="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</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="l00366"></a><span class="lineno"> 366</span>  int32_t output_multiplier;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  int32_t output_shift;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="namespacearm__compute_1_1quantization.xhtml#a63fdf412c27b0151bd4495c64cc112da">quantization::calculate_quantized_multiplier</a>(multiplier, &output_multiplier, &output_shift));</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">GEMMLowpOutputStageInfo</a> gemmlowp_output_stage_info;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a1cfb92f1c287bf099c3fca0ef0391a2b">gemmlowp_multiplier</a> = output_multiplier;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a3f0613aeb69c326e7d8ffb34b44fae94">gemmlowp_shift</a> = output_shift;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a01934c5087f5193aaf3ea9bf41d1a8dc">gemmlowp_offset</a> = oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  gemmlowp_output_stage_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a6e019ad85979fd73c74f97e5483faf35">type</a> = <a class="code" href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a>;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a8014f142f8a43df8229c8d418f747f8a">NEGEMMLowpOutputStage::validate</a>(&gemmlowp_output, biases, output, gemmlowp_output_stage_info));</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  }</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>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</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"><a class="line" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 381</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">NEFullyConnectedLayer::run</a>()</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</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="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="comment">// Linearize input if it comes from a convolutional layer</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  {</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</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>(&_flatten_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  }</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>  <span class="comment">// Run matrix multiply</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">if</span>(_is_quantized)</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>  _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  }</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  {</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  }</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="comment">// Accumulate biases if provided</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  _gemmlowp_output_stage.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  }</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  {</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">if</span>(_accumulate_biases)</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>  <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>(&_accumulate_biases_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  }</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> </div><div class="line"><a name="l00417"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 417</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">NEFullyConnectedLayer::prepare</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>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">if</span>(!_weights_manager)</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>  <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="l00424"></a><span class="lineno"> 424</span>  }</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="keyword">auto</span> release_unused = [](<a class="code" href="classarm__compute_1_1_tensor.xhtml">Tensor</a> * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>)</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  {</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</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="l00429"></a><span class="lineno"> 429</span>  {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>->allocator()->free();</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>  };</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="comment">// Pointer to current weights</span></div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *cur_weights = _original_weights;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="comment">// Reshape of the weights (happens only once)</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  {</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(_original_weights))</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  {</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  cur_weights = _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">run</a>(cur_weights, &_reshape_weights_managed_function);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="comment">// Reshape of the weights (happens only once)</span></div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  {</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">// Run reshape weights kernel and mark weights as unused</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  _reshape_weights_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="l00451"></a><span class="lineno"> 451</span>  _reshape_weights_function.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  }</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</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="l00454"></a><span class="lineno"> 454</span>  cur_weights = &_reshape_weights_output;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  }</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  _are_weights_reshaped = <span class="keyword">true</span>;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  }</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="comment">// Convert weights if needed (happens only once)</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">if</span>(!_are_weights_converted)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(cur_weights))</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">run</a>(cur_weights, &_convert_weights_managed);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  }</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  _converted_weights_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="l00469"></a><span class="lineno"> 469</span>  _convert_weights.<a class="code" href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  cur_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  }</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  _are_weights_converted = <span class="keyword">true</span>;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  }</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="comment">// Release reshaped weights if unused</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  release_unused(&_reshape_weights_output);</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="comment">// Prepare GEMM prepare and release unused weights</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keywordflow">if</span>(!_is_quantized)</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  {</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  }</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="comment">// Release converted weights if unused</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  release_unused(&_reshape_weights_output);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  release_unused(&_converted_weights_output);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  }</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> }</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> } <span class="comment">// namespace arm_compute</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="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml_a1cfb92f1c287bf099c3fca0ef0391a2b"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a1cfb92f1c287bf099c3fca0ef0391a2b">arm_compute::GEMMLowpOutputStageInfo::gemmlowp_multiplier</a></div><div class="ttdeci">int32_t gemmlowp_multiplier</div><div class="ttdoc">GEMMLowp output stage multiplier used for quantizing to QASYMM8.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01948">Types.h:1948</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca"><div class="ttname"><a href="namespacearm__compute.xhtml#a5558e2cc22f7f4771653d992c8ad8864ab300cae200f67712c1eb9234e28158ca">arm_compute::GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT</a></div><div class="ttdoc">Quantize using a fixed point multiplication.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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_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="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#l00808">Types.h:808</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="_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="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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="classarm__compute_1_1_tensor_info_xhtml_ac74736e3863207232a23b7181c1d0f44"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">arm_compute::TensorInfo::quantization_info</a></div><div class="ttdeci">QuantizationInfo quantization_info() const override</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00311">TensorInfo.h:311</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00792">Validate.h:792</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00466">Error.h:466</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml">arm_compute::FullyConnectedLayerInfo</a></div><div class="ttdoc">Fully connected layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00803">Types.h:803</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="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml"><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#l00042">QuantizationInfo.h:42</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml_a1d28dec57cce925ad92342891bd71e7c"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">arm_compute::UniformQuantizationInfo::scale</a></div><div class="ttdeci">float scale</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00064">QuantizationInfo.h:64</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml_a01934c5087f5193aaf3ea9bf41d1a8dc"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a01934c5087f5193aaf3ea9bf41d1a8dc">arm_compute::GEMMLowpOutputStageInfo::gemmlowp_offset</a></div><div class="ttdeci">int32_t gemmlowp_offset</div><div class="ttdoc">GEMMLowp output stage offset used for quantizing to QASYMM8.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01947">Types.h:1947</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a18bd2d54155972d4d740fbba8337a27e"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a18bd2d54155972d4d740fbba8337a27e">arm_compute::IWeightsManager::manage</a></div><div class="ttdeci">void manage(const ITensor *weights, ITransformWeights *parent=nullptr)</div><div class="ttdoc">Start managing a weights tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00033">IWeightsManager.cpp:33</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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#l00296">Error.h:296</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml_a6e019ad85979fd73c74f97e5483faf35"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a6e019ad85979fd73c74f97e5483faf35">arm_compute::GEMMLowpOutputStageInfo::type</a></div><div class="ttdeci">GEMMLowpOutputStageType type</div><div class="ttdoc">GEMMLowp output stage type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01946">Types.h:1946</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2020 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</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="_size2_d_8h_xhtml"><div class="ttname"><a href="_size2_d_8h.xhtml">Size2D.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a78839e7861ba8ffed52ca55da2745761"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">arm_compute::TensorInfo::set_quantization_info</a></div><div class="ttdeci">ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info) override</div><div class="ttdoc">Set the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00372">TensorInfo.cpp:372</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_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#l00426">ShapeCalculator.h:426</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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_n_e_fully_connected_layer_reshape_weights_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEFullyConnectedLayerReshapeWeights::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEFullyConnectedLayerRes...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00073">NEFullyConnectedLayer.cpp:73</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div> |
| <div class="ttc" id="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_i_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a83efb6708574e67d13965bcd2059ad75"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75">arm_compute::misc::shape_calculator::compute_flatten_shape</a></div><div class="ttdeci">TensorShape compute_flatten_shape(const ITensorInfo *input)</div><div class="ttdoc">Calculate the flattened output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00587">ShapeCalculator.h:587</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_a3493ba7d1f2057740ff5931fa00a44ac"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">arm_compute::NEGEMM::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMM.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00172">NEGEMM.cpp:172</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml">arm_compute::QuantizationInfo</a></div><div class="ttdoc">Quantization information.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00069">QuantizationInfo.h:69</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="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00152">Error.h:152</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_flatten_layer_kernel_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_flatten_layer_kernel.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NEFlattenLayerKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_flatten_layer_kernel_8cpp_source.xhtml#l00081">NEFlattenLayerKernel.cpp:81</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core_xhtml_ae939cbc6a8a6747f193bfe8b54a7881c"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ae939cbc6a8a6747f193bfe8b54a7881c">arm_compute::NEGEMMLowpMatrixMultiplyCore::configure</a></div><div class="ttdeci">void configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *output, 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_lowp_matrix_multiply_core_8cpp_source.xhtml#l00051">NEGEMMLowpMatrixMultiplyCore.cpp:51</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_n_e_g_e_m_m_lowp_matrix_multiply_core_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMMLowpMatrixMultiplyCore::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00479">NEGEMMLowpMatrixMultiplyCore.cpp:479</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</div></div> |
| <div class="ttc" id="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#l00807">Types.h:807</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="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_a9845f83f6f3bc45f9fb57ea1345e3dd3"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#a9845f83f6f3bc45f9fb57ea1345e3dd3">arm_compute::NEFullyConnectedLayer::NEFullyConnectedLayer</a></div><div class="ttdeci">NEFullyConnectedLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr, IWeightsManager *weights_manager=nullptr)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00078">NEFullyConnectedLayer.cpp:78</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml_a706fc156bcd4c45441bcaad05884b57d"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">arm_compute::QuantizationInfo::uniform</a></div><div class="ttdeci">UniformQuantizationInfo uniform() const</div><div class="ttdoc">Return per layer quantization info.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00148">QuantizationInfo.h:148</a></div></div> |
| <div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</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="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">arm_compute::GEMMLowpOutputStageInfo</a></div><div class="ttdoc">GEMMLowp output stage info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01944">Types.h:1944</a></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_1weights__transformations_1_1_n_e_convert_fully_connected_weights_managed_xhtml_a4db9a257c48616afe7fdd7e445fad8fe"><div class="ttname"><a href="classarm__compute_1_1weights__transformations_1_1_n_e_convert_fully_connected_weights_managed.xhtml#a4db9a257c48616afe7fdd7e445fad8fe">arm_compute::weights_transformations::NEConvertFullyConnectedWeightsManaged::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const TensorShape &original_input_shape, DataLayout data_layout)</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8h_source.xhtml#l00098">NEConvertFullyConnectedWeights.h:98</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml">arm_compute::Tensor</a></div><div class="ttdoc">Basic implementation of the tensor interface.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8h_source.xhtml#l00037">Tensor.h:37</a></div></div> |
| <div class="ttc" id="_n_e_scheduler_8h_xhtml"><div class="ttname"><a href="_n_e_scheduler_8h.xhtml">NEScheduler.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1weights__transformations_1_1_n_e_fully_connected_layer_reshape_weights_managed_xhtml_afad51fa14022cfbe5a27548024002aa2"><div class="ttname"><a href="classarm__compute_1_1weights__transformations_1_1_n_e_fully_connected_layer_reshape_weights_managed.xhtml#afad51fa14022cfbe5a27548024002aa2">arm_compute::weights_transformations::NEFullyConnectedLayerReshapeWeightsManaged::configure</a></div><div class="ttdeci">void configure(const ITensor *input)</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8h_source.xhtml#l00094">NEFullyConnectedLayer.h:94</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_transpose_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_transpose_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NETransposeKernel::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 NETransposeKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_transpose_kernel_8cpp_source.xhtml#l00486">NETransposeKernel.cpp:486</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml">arm_compute::IWeightsManager</a></div><div class="ttdoc">Weights manager interface to handle weights transformations.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8h_source.xhtml#l00036">IWeightsManager.h:36</a></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="classarm__compute_1_1_n_e_convert_fully_connected_weights_xhtml_ac0cfd5b64b24f3dfae04ee3d0f258624"><div class="ttname"><a href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ac0cfd5b64b24f3dfae04ee3d0f258624">arm_compute::NEConvertFullyConnectedWeights::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const TensorShape &original_input_shape, DataLayout data_layout)</div><div class="ttdoc">Initialize the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00033">NEConvertFullyConnectedWeights.cpp:33</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">Utils.h:1139</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_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="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml_a3f0613aeb69c326e7d8ffb34b44fae94"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml#a3f0613aeb69c326e7d8ffb34b44fae94">arm_compute::GEMMLowpOutputStageInfo::gemmlowp_shift</a></div><div class="ttdeci">int32_t gemmlowp_shift</div><div class="ttdoc">GEMMLowp output stage shift used for quantizing to uint8.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01949">Types.h:1949</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_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_n_e_g_e_m_m_lowp_output_stage_xhtml_a215ad3877ff40dcc4e6e39f25f2a27f0"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a215ad3877ff40dcc4e6e39f25f2a27f0">arm_compute::NEGEMMLowpOutputStage::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo &info)</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00086">NEGEMMLowpOutputStage.cpp:86</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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#l00082">IMemoryGroup.h:82</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_g_e_m_m_lowp_output_stage_xhtml_a8014f142f8a43df8229c8d418f747f8a"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_output_stage.xhtml#a8014f142f8a43df8229c8d418f747f8a">arm_compute::NEGEMMLowpOutputStage::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMLowpOutputStage.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_output_stage_8cpp_source.xhtml#l00142">NEGEMMLowpOutputStage.cpp:142</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_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_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="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#l00805">Types.h:805</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_convert_fully_connected_weights_xhtml_a01b81c1c60fd95dc360fd7ad680f114b"><div class="ttname"><a href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#a01b81c1c60fd95dc360fd7ad680f114b">arm_compute::NEConvertFullyConnectedWeights::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const TensorShape &original_input_shape, DataLayout data_layout)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEConvertFullyConnectedW...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00039">NEConvertFullyConnectedWeights.cpp:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel_xhtml_a67759131249ecc51533fe269d0d90c51"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#a67759131249ecc51533fe269d0d90c51">arm_compute::NEGEMMMatrixAccumulateBiasesKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *accum, const ITensorInfo *biases)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMMatrixAccumulateBi...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00100">NEGEMMMatrixAccumulateBiasesKernel.cpp:100</a></div></div> |
| <div class="ttc" id="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="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#l00065">QuantizationInfo.h:65</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMM::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM.cpp:335</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a63fdf412c27b0151bd4495c64cc112da"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a63fdf412c27b0151bd4495c64cc112da">arm_compute::quantization::calculate_quantized_multiplier</a></div><div class="ttdeci">Status calculate_quantized_multiplier(float multiplier, int32_t *quant_multiplier, int32_t *shift)</div><div class="ttdoc">Calculate quantized representation of multiplier.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00038">AsymmHelpers.cpp:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_afff44a0110c3767f144c9cb8e6f81625"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#afff44a0110c3767f144c9cb8e6f81625">arm_compute::test::validation::weights_quantization_info</a></div><div class="ttdeci">const QuantizationInfo weights_quantization_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00198">ConvolutionLayer.cpp:198</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="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#l00806">Types.h:806</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer_reshape_weights.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NEFullyConnectedLayerReshapeWeights::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00066">NEFullyConnectedLayer.cpp:66</a></div></div> |
| <div class="ttc" id="_n_e_fully_connected_layer_8h_xhtml"><div class="ttname"><a href="_n_e_fully_connected_layer_8h.xhtml">NEFullyConnectedLayer.h</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="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml">arm_compute::misc::shape_calculator</a></div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00040">ShapeCalculator.h:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core_xhtml_a8c3cf2d65afb288e39909171ada19566"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#a8c3cf2d65afb288e39909171ada19566">arm_compute::NEGEMMLowpMatrixMultiplyCore::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMLowpMatrixMultiply...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00281">NEGEMMLowpMatrixMultiplyCore.cpp:281</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a73808ac61e51d72c7d6349d6d51dcf37"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">arm_compute::IWeightsManager::run</a></div><div class="ttdeci">ITensor * run(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Run the reshape function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00051">IWeightsManager.cpp:51</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">arm_compute::DataType::QASYMM8_SIGNED</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number signed</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_fully_connected_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEFullyConnectedLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fully_connected_layer_8cpp_source.xhtml#l00417">NEFullyConnectedLayer.cpp:417</a></div></div> |
| <div class="ttc" id="_asymm_helpers_8h_xhtml"><div class="ttname"><a href="_asymm_helpers_8h.xhtml">AsymmHelpers.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_flatten_layer_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_flatten_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEFlattenLayerKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEFlattenLayerKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_flatten_layer_kernel_8cpp_source.xhtml#l00095">NEFlattenLayerKernel.cpp:95</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a1b67d5b720119d50faa286c774579ecc"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">arm_compute::Dimensions< uint32_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_n_e_g_e_m_m_matrix_accumulate_biases_kernel_xhtml_aed1adf983092c2f8af29eba1dc29920c"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aed1adf983092c2f8af29eba1dc29920c">arm_compute::NEGEMMMatrixAccumulateBiasesKernel::configure</a></div><div class="ttdeci">void configure(ITensor *accum, const ITensor *biases)</div><div class="ttdoc">Set the accumulate buffer and the biases of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00084">NEGEMMMatrixAccumulateBiasesKernel.cpp:84</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a9b3e3aff065fafe490a5e190bf530ada"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">arm_compute::IWeightsManager::acquire</a></div><div class="ttdeci">ITensor * acquire(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Acquire the requested reshape tensor of the selected weights.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00117">IWeightsManager.cpp:117</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_convert_fully_connected_weights_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_convert_fully_connected_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEConvertFullyConnectedWeights::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_convert_fully_connected_weights_8cpp_source.xhtml#l00045">NEConvertFullyConnectedWeights.cpp:45</a></div></div> |
| <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> |
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