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| <a href="_n_e_depthwise_convolution_assembly_dispatch_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) 2019-2020 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> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_n_e_depthwise_convolution_assembly_dispatch_8h.xhtml">arm_compute/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_c_p_p_2_validate_8h.xhtml">arm_compute/core/CPP/Validate.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_i_tensor_8h.xhtml">arm_compute/core/ITensor.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "arm_compute/core/NEON/kernels/assembly/NEDepthwiseConvolutionAssemblyKernelWrapper.h"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "arm_compute/core/NEON/kernels/convolution/depthwise/depthwise_dilated.hpp"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "arm_compute/core/NEON/kernels/convolution/depthwise/depthwise_quantized_dilated.hpp"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_info_helpers_8h.xhtml">arm_compute/core/utils/misc/InfoHelpers.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</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="l00035"></a><span class="lineno"> 35</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="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</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="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <set></span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> std::unique_ptr<depthwise::IDepthwiseConvolution> get_qasymm8_convolver(<span class="keywordtype">int</span> kernel_size, <span class="keywordtype">int</span> stride_x,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordtype">int</span> n_batches, <span class="keywordtype">int</span> in_rows, <span class="keywordtype">int</span> in_cols, <span class="keywordtype">int</span> n_channels,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">int</span> dilation_factor, neon_convolution_kernels::ActivationFunction activation,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">const</span> qasymm8::QAsymm8Params &wqinfo, <span class="keyword">const</span> qasymm8::QAsymm8Params &iqinfo, <span class="keyword">const</span> qasymm8::QAsymm8Params &oqinfo,</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">const</span> qasymm8::QAsymm8RescaleParams &rescale_params,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">int</span> padding_top, <span class="keywordtype">int</span> padding_left, <span class="keywordtype">int</span> padding_bottom, <span class="keywordtype">int</span> padding_right)</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> {</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">switch</span>(kernel_size)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">case</span> 3:</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">switch</span>(stride_x)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QAsymm8DilatedDepthwiseConvolution<2, 2, 3, 3, 1, 1>>(</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QAsymm8DilatedDepthwiseConvolution<2, 2, 3, 3, 2, 2>>(</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</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="keywordflow">case</span> 5:</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">switch</span>(stride_x)</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>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QAsymm8DilatedDepthwiseConvolution<2, 2, 5, 5, 1, 1>>(</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QAsymm8DilatedDepthwiseConvolution<2, 2, 5, 5, 2, 2>>(</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</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> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> std::unique_ptr<depthwise::IDepthwiseConvolution> get_qsymm8_perchannel_convolver(<span class="keywordtype">int</span> kernel_size, <span class="keywordtype">int</span> stride_x,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordtype">int</span> n_batches, <span class="keywordtype">int</span> in_rows, <span class="keywordtype">int</span> in_cols, <span class="keywordtype">int</span> n_channels,</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  neon_convolution_kernels::ActivationFunction activation,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">const</span> qsymm8::QSymm8PerChannelParams &wqinfo, <span class="keyword">const</span> qasymm8::QAsymm8Params &iqinfo, <span class="keyword">const</span> qasymm8::QAsymm8Params &oqinfo,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">const</span> qsymm8::QSymm8PerChannelRescaleParams &rescale_params,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordtype">int</span> padding_top, <span class="keywordtype">int</span> padding_left, <span class="keywordtype">int</span> padding_bottom, <span class="keywordtype">int</span> padding_right)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">switch</span>(kernel_size)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">case</span> 3:</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="keywordflow">switch</span>(stride_x)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QSymm8HybridPerChannelDepthwiseConvolution<2, 2, 3, 3, 1, 1>>(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  n_batches, in_rows, in_cols, n_channels, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QSymm8HybridPerChannelDepthwiseConvolution<2, 2, 3, 3, 2, 2>>(</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  n_batches, in_rows, in_cols, n_channels, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">case</span> 5:</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="keywordflow">switch</span>(stride_x)</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>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QSymm8HybridPerChannelDepthwiseConvolution<2, 2, 5, 5, 1, 1>>(</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  n_batches, in_rows, in_cols, n_channels, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::QSymm8HybridPerChannelDepthwiseConvolution<2, 2, 5, 5, 2, 2>>(</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  n_batches, in_rows, in_cols, n_channels, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> }</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="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> std::unique_ptr<depthwise::IDepthwiseConvolution> get_fp16_convolver(<span class="keywordtype">int</span> kernel_size, <span class="keywordtype">int</span> stride_x,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">int</span> n_batches, <span class="keywordtype">int</span> in_rows, <span class="keywordtype">int</span> in_cols, <span class="keywordtype">int</span> n_channels,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordtype">int</span> dilation_factor, neon_convolution_kernels::ActivationFunction activation,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordtype">int</span> padding_top, <span class="keywordtype">int</span> padding_left, <span class="keywordtype">int</span> padding_bottom, <span class="keywordtype">int</span> padding_right)</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>  <span class="keywordflow">switch</span>(kernel_size)</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="keywordflow">case</span> 3:</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">switch</span>(stride_x)</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<3, 3, 3, 3, 1, 1, float16_t, float16_t, float16_t>>(</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<3, 3, 3, 3, 2, 2, float16_t, float16_t, float16_t>>(</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">case</span> 5:</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">switch</span>(stride_x)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  {</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<3, 3, 5, 5, 1, 1, float16_t, float16_t, float16_t>>(</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<3, 3, 5, 5, 2, 2, float16_t, float16_t, float16_t>>(</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</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>  }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</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> <span class="preprocessor">#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> std::unique_ptr<depthwise::IDepthwiseConvolution> get_fp32_convolver(<span class="keywordtype">int</span> kernel_size, <span class="keywordtype">int</span> stride_x,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordtype">int</span> n_batches, <span class="keywordtype">int</span> in_rows, <span class="keywordtype">int</span> in_cols, <span class="keywordtype">int</span> n_channels,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordtype">int</span> dilation_factor, neon_convolution_kernels::ActivationFunction activation,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordtype">int</span> padding_top, <span class="keywordtype">int</span> padding_left, <span class="keywordtype">int</span> padding_bottom, <span class="keywordtype">int</span> padding_right)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">switch</span>(kernel_size)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">case</span> 3:</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">switch</span>(stride_x)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<4, 4, 3, 3, 1, 1, float, float, float>>(</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<3, 3, 3, 3, 2, 2, float, float, float>>(</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</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>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">case</span> 5:</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordflow">switch</span>(stride_x)</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">case</span> 1:</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<4, 4, 5, 5, 1, 1, float, float, float>>(</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordflow">case</span> 2:</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<depthwise::DilatedDepthwiseConvolution<3, 3, 5, 5, 2, 2, float, float, float>>(</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</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>  }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</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> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> std::unique_ptr<depthwise::IDepthwiseConvolution> create_convolver(<span class="keyword">const</span> ITensor *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keyword">const</span> ITensor *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  ITensor *output,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  PadStrideInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  ActivationLayerInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> Size2D &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type();</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">const</span> TensorShape <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape();</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> n_batches = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[3];</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_rows = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.z();</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_cols = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.y();</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> n_channels = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.x();</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> dilation_factor = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x();</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_top = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top();</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_left = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left();</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_bottom = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom();</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_right = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right();</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_uniform_quantized = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</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#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_perchannel_quantized = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</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#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">DataType::QSYMM8_PER_CHANNEL</a>);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> </div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_x = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first;</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_size = <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#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>();</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>  <span class="comment">// Map activation function</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  neon_convolution_kernels::ActivationFunction activation = neon_convolution_kernels::ActivationFunction::None;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#abb0a3afec6da2c1c38345abccf38ce71">arm_compute::utils::info_helpers::is_relu</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>))</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>  activation = neon_convolution_kernels::ActivationFunction::ReLU;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  }</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#adaa2f985265ab514c1a0d5a48703dff1">arm_compute::utils::info_helpers::is_relu6</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>))</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  {</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  activation = neon_convolution_kernels::ActivationFunction::ReLU6;</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="comment">// Create quantized convolver</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">if</span>(is_uniform_quantized)</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  {</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">const</span> UniformQuantizationInfo input_qinfo = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info().uniform();</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">const</span> UniformQuantizationInfo weights_qinfo = <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="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> UniformQuantizationInfo output_qinfo = output->info()->quantization_info().uniform();</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="comment">// Check that quantization info are in the range [0, 255]</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input_qinfo.offset < 0 || input_qinfo.offset > 255);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights_qinfo.offset < 0 || weights_qinfo.offset > 255);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output_qinfo.offset < 0 || output_qinfo.offset > 255);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">const</span> qasymm8::QAsymm8Params iqinfo{ static_cast<uint8_t>(input_qinfo.offset), input_qinfo.scale };</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">const</span> qasymm8::QAsymm8Params wqinfo{ static_cast<uint8_t>(weights_qinfo.offset), weights_qinfo.scale };</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keyword">const</span> qasymm8::QAsymm8Params oqinfo{ static_cast<uint8_t>(output_qinfo.offset), output_qinfo.scale };</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="comment">// Calculate rescale parameters</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> fmultipler = iqinfo.scale * wqinfo.scale / oqinfo.scale;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  int32_t qmultiplier = 0;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  int32_t qshift = 0;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#acd91a85bed01075d0b9384f065f94039">quantization::calculate_quantized_multiplier_less_than_one</a>(fmultipler, &qmultiplier, &qshift);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  qasymm8::QAsymm8RescaleParams rescale_params(qshift, qmultiplier, fmultipler);</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>  <span class="keywordflow">return</span> get_qasymm8_convolver(kernel_size, stride_x, n_batches, in_rows, in_cols, n_channels, dilation_factor, activation,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  }</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(is_perchannel_quantized)</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  {</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">const</span> UniformQuantizationInfo input_qinfo = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info().uniform();</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">const</span> QuantizationInfo weights_qinfo = <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="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> UniformQuantizationInfo output_qinfo = output->info()->quantization_info().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="comment">// Check that quantization info are in the range [0, 255]</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input_qinfo.offset < 0 || input_qinfo.offset > 255);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output_qinfo.offset < 0 || output_qinfo.offset > 255);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keyword">const</span> qasymm8::QAsymm8Params iqinfo{ static_cast<uint8_t>(input_qinfo.offset), input_qinfo.scale };</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> qsymm8::QSymm8PerChannelParams wqinfo{ weights_qinfo.scale() };</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> qasymm8::QAsymm8Params oqinfo{ static_cast<uint8_t>(output_qinfo.offset), output_qinfo.scale };</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// Calculate rescale parameters</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  std::vector<float> fmultipliers;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  std::vector<int32_t> qmultipliers;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  std::vector<int32_t> qshifts;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> <span class="keyword">const</span> s : wqinfo.scales)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> fmultipler = iqinfo.scale * s / oqinfo.scale;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  int32_t qmultiplier = 0;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  int32_t qshift = 0;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#acd91a85bed01075d0b9384f065f94039">quantization::calculate_quantized_multiplier_less_than_one</a>(fmultipler, &qmultiplier, &qshift);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  fmultipliers.push_back(fmultipler);</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  qmultipliers.push_back(qmultiplier);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  qshifts.push_back(qshift);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  }</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  qsymm8::QSymm8PerChannelRescaleParams rescale_params(qshifts, qmultipliers, fmultipliers);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">return</span> get_qsymm8_perchannel_convolver(kernel_size, stride_x, n_batches, in_rows, in_cols, n_channels, activation,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  }</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">else</span></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">// Create float convolver</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">switch</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>)</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="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  {</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keywordflow">return</span> get_fp16_convolver(kernel_size, stride_x, n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> <span class="preprocessor">#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</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>  <span class="keywordflow">return</span> get_fp32_convolver(kernel_size, stride_x, n_batches, in_rows, in_cols, n_channels, dilation_factor, activation, padding_top, padding_left, padding_bottom, padding_right);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  }</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</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>  }</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> } <span class="comment">// namespace</span></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> <span class="keyword">struct </span>NEDepthwiseConvolutionAssemblyDispatch::LocalImpl</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> {</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  std::unique_ptr<depthwise::IDepthwiseConvolution> _dwc_assembly_kernel{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  NEDepthwiseConvolutionAssemblyKernelWrapper _dwc_acl_kernel{};</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> };</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <span class="preprocessor">#ifndef DOXYGEN_SKIP_THIS</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a4e3fd833ab574a3299997f4a4faf83df">NEDepthwiseConvolutionAssemblyDispatch::NEDepthwiseConvolutionAssemblyDispatch</a>(std::shared_ptr<arm_compute::IMemoryManager> memory_manager)</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  : _memory_group(std::move(memory_manager)), _input(nullptr), _weights(nullptr), _bias(nullptr), _output(nullptr), _packed_weights(), _workspace(), _is_prepared(false),</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  _pImpl(support::cpp14::<a class="code" href="namespacearm__compute_1_1support_1_1cpp14.xhtml#a1d95a84d1a4610af6a128ad96c907e8b">make_unique</a><LocalImpl>())</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> }</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> <span class="preprocessor">#endif </span><span class="comment">/* DOXYGEN_SKIP_THIS */</span><span class="preprocessor"></span></div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a74a504608c72ef3a540729f2b39fe7c9">NEDepthwiseConvolutionAssemblyDispatch::~NEDepthwiseConvolutionAssemblyDispatch</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a34b1b5dae78114740544785111bd9a9e"> 346</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a34b1b5dae78114740544785111bd9a9e">NEDepthwiseConvolutionAssemblyDispatch::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>,</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <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>,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <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#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>,</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier,</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>)</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>  <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="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(depth_multiplier);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</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_depthwise_convolution_assembly_dispatch.xhtml#ac6f25b054a1ebb8fa338c228a41afa06">NEDepthwiseConvolutionAssemblyDispatch::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(),</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</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="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> != <span class="keyword">nullptr</span> ? <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  depth_multiplier,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// Output auto inizialitation if not yet initialized</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">misc::shape_calculator::compute_depthwise_convolution_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(), *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</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(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>).set_quantization_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>()));</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>  _input = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  _weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  _bias = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  _output = output;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="comment">// Create convolver</span></div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  _pImpl->_dwc_assembly_kernel = create_convolver(<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, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_pImpl->_dwc_assembly_kernel == <span class="keyword">nullptr</span>);</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>  <span class="comment">// Create assembly kernel wrapper</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  _pImpl->_dwc_acl_kernel.configure(_pImpl->_dwc_assembly_kernel.get());</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>  constexpr <span class="keywordtype">size_t</span> alignment = 128;</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>  <span class="comment">// Create workspace</span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_threads = <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#ac24584a63e484123e3756d1b2a1c9e2f">num_threads</a>();</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> workspace_size = _pImpl->_dwc_assembly_kernel->get_working_space_size(num_threads);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>(workspace_size == 0, <span class="stringliteral">"Workspace size cannot be 0 !"</span>);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  _workspace.<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="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>{ workspace_size }, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>), alignment);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_workspace);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  _workspace.<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="l00392"></a><span class="lineno"> 392</span> </div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="comment">// Create packing tensor</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> pack_tensor_size = _pImpl->_dwc_assembly_kernel->get_packed_params_size();</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>(pack_tensor_size == 0, <span class="stringliteral">"Pack tensor size cannot be 0 !"</span>);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  _packed_weights.<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="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>{ pack_tensor_size }, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>), alignment);</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> </div><div class="line"><a name="l00399"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#ac6f25b054a1ebb8fa338c228a41afa06"> 399</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#ac6f25b054a1ebb8fa338c228a41afa06">NEDepthwiseConvolutionAssemblyDispatch::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>,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <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>,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <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#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <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="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</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>  <a class="code" href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>);</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</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#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->data_type() != <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">DataType::QSYMM8_PER_CHANNEL</a>)</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="_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>);</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>  <a class="code" href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</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>);</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>  <span class="comment">// Validate convolver</span></div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(!<a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a1fb5996aa6bd294a9ef2f7c6ba627578">is_optimized_supported</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</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="comment">// Validate activation</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#abb0a3afec6da2c1c38345abccf38ce71">is_relu</a> = <a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#abb0a3afec6da2c1c38345abccf38ce71">arm_compute::utils::info_helpers::is_relu</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#adaa2f985265ab514c1a0d5a48703dff1">is_relu6</a> = <a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#adaa2f985265ab514c1a0d5a48703dff1">arm_compute::utils::info_helpers::is_relu6</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</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#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled() && !(<a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#abb0a3afec6da2c1c38345abccf38ce71">is_relu</a> || <a class="code" href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#adaa2f985265ab514c1a0d5a48703dff1">is_relu6</a>));</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="comment">// Check bias</span></div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  {</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>->num_dimensions() > 1);</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>->dimension(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(channel_idx));</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  }</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="comment">// Check output</span></div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">if</span>(output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>() != 0)</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</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_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">misc::shape_calculator::compute_depthwise_convolution_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</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>, output);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  }</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="comment">// The uniform quantization case will only have 1 scale value in the weights quantization info</span></div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> input_qinfo = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->quantization_info().uniform();</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> weights_qinfo = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->quantization_info();</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> output_qinfo = 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="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> <span class="keyword">const</span> s : weights_qinfo.<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#af21c7fddee28e9aa0a37c633300db0e0">scale</a>())</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="keyword">const</span> <span class="keywordtype">float</span> fmultipler = input_qinfo.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> * s / output_qinfo.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(fmultipler > 1.f);</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> </div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> }</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a1fb5996aa6bd294a9ef2f7c6ba627578"> 453</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a1fb5996aa6bd294a9ef2f7c6ba627578">NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported</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>,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <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>,</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>)</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>  <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>);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">// Reshape input shape if in NHWC format</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout();</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> in_shape{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape() };</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</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>  in_shape.set(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape().y());</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  in_shape.set(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape().z());</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  in_shape.set(<a class="code" href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">Window::DimZ</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape().x());</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  }</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> </div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="comment">// Check data type</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="comment">// TODO (COMPMID-3004): Add assembly optimized routine for QASYMM8_SIGNED NEDepthwiseConvolutionLayer</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> input_type = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_type();</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_input_type_valid = <a class="code" href="namespacearm__compute.xhtml#af5982a092e9eb743fce2d6392bdd8897">is_data_type_float</a>(input_type) || input_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> weights_type = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->data_type();</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_weights_type_valid = <a class="code" href="namespacearm__compute.xhtml#af5982a092e9eb743fce2d6392bdd8897">is_data_type_float</a>(weights_type) || weights_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a> || weights_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">DataType::QASYMM8_SIGNED</a></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  || weights_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">DataType::QSYMM8_PER_CHANNEL</a>;</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">// Check weighs size</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  std::set<unsigned int> supported_kernel_sizes = { 3, 5 };</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_w = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(width_idx);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_h = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(height_idx);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordtype">bool</span> weights_supported = (kernel_w == kernel_h) && (supported_kernel_sizes.count(kernel_w) != 0);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span> </div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="comment">// Check for supported strides</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keyword">const</span> <span class="keyword">auto</span> &strides = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride();</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordtype">bool</span> supported_strides = (strides.first == strides.second) && ((strides.first == 1) || (strides.first == 2));</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>  <span class="comment">// Check for supported padding</span></div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pad_top = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top();</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pad_right = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right();</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pad_bottom = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom();</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keyword">const</span> <span class="keyword">auto</span> pad_left = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left();</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> same_pad = <a class="code" href="namespacearm__compute.xhtml#aa6d4f0b9fedd979c5b768f9b34fda9f6">calculate_same_pad</a>(in_shape, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(kernel_w, kernel_h), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="keywordtype">bool</span> is_same_padding = (pad_top == same_pad.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a3fea0ce4e6eeee7bf3a511c31b51d44f">pad_top</a>()) && (pad_right == same_pad.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a3860caeeaa259d59ccf69c3aea23f549">pad_right</a>()) && (pad_bottom == same_pad.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#ac49df0924d183da42cffc04cf9aba1f8">pad_bottom</a>()) && (pad_left == same_pad.<a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml#a7144874ab401f5c4e249a1115dfb5166">pad_left</a>());</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keywordtype">bool</span> is_valid_padding = (pad_top == 0) && (pad_right == 0) && (pad_bottom == 0) && (pad_left == 0);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keywordtype">bool</span> supported_padding = is_same_padding || is_valid_padding;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="comment">// TODO(COMPMID-2464): Enable once dilated conv with stride 2 is supported</span></div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordtype">bool</span> is_dilation_supported = ((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a> == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>)) || ((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x() == <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.y()) && strides.first == 1));</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keywordflow">if</span>(weights_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">DataType::QSYMM8_PER_CHANNEL</a>)</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  {</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  is_dilation_supported = is_dilation_supported && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a> == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  }</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> </div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keywordflow">return</span> is_input_type_valid && is_weights_type_valid && weights_supported && supported_strides && supported_padding && (depth_multiplier == 1) && is_dilation_supported;</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> }</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> </div><div class="line"><a name="l00511"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#ad1717410afd0be936c6213a63c8005fb"> 511</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#ad1717410afd0be936c6213a63c8005fb">NEDepthwiseConvolutionAssemblyDispatch::run</a>()</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> {</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="comment">// Prepare assembly kernel</span></div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</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="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <span class="comment">// Setup inputs/outputs</span></div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>() == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  _pImpl->_dwc_assembly_kernel->set_working_space(static_cast<void *>(_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>()));</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> </div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_element_size = _input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>();</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_batch_stride = _input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>()[3] / input_element_size;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_row_stride = _input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#abb29a685080e999c2a0cb874d2f7bb5a">z</a>() / input_element_size;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_col_stride = _input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>() / input_element_size;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keyword">const</span> <span class="keywordtype">void</span> *input_ptr = _input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + _input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#ad0bd5cc32e7e4c0699eccba91e5de397">offset_first_element_in_bytes</a>();</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  _pImpl->_dwc_assembly_kernel->set_input(input_ptr, input_batch_stride, input_row_stride, input_col_stride);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_element_size = _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#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>();</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_batch_stride = _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#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>()[3] / output_element_size;</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_row_stride = _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#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#abb29a685080e999c2a0cb874d2f7bb5a">z</a>() / output_element_size;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_col_stride = _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#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>() / output_element_size;</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordtype">void</span> *output_ptr = _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + _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#ad0bd5cc32e7e4c0699eccba91e5de397">offset_first_element_in_bytes</a>();</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  _pImpl->_dwc_assembly_kernel->set_output(output_ptr, output_batch_stride, output_row_stride, output_col_stride);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="comment">// Schedule assembly kernel</span></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</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>(&_pImpl->_dwc_acl_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> }</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 542</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">NEDepthwiseConvolutionAssemblyDispatch::prepare</a>()</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> {</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  {</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  _packed_weights.<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="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_packed_weights.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>() == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> </div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="comment">// Pack weights and bias</span></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> weights_element_size = _weights-><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#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>();</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> weights_row_stride = _weights-><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#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#abb29a685080e999c2a0cb874d2f7bb5a">z</a>() / weights_element_size;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> weights_col_stride = _weights-><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#a6b14f175bf5281f57b561e2d4e4b1f1f">strides_in_bytes</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>() / weights_element_size;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  _pImpl->_dwc_assembly_kernel->pack_params(_packed_weights.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>(),</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  _weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + _weights-><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#ad0bd5cc32e7e4c0699eccba91e5de397">offset_first_element_in_bytes</a>(),</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  weights_row_stride,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  weights_col_stride,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  (_bias != <span class="keyword">nullptr</span>) ? _bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  _pImpl->_dwc_assembly_kernel->set_packed_params_buffer(_packed_weights.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>());</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  _weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <span class="keywordflow">if</span>(_bias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  {</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  _bias-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  }</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  }</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> }</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">AbsoluteDifference.cpp:97</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="_i_tensor_8h_xhtml"><div class="ttname"><a href="_i_tensor_8h.xhtml">ITensor.h</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="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ac7147815227e7ba91814cfdcd38f23ed"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape</a></div><div class="ttdeci">TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info, unsigned int depth_multiplier, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Calculate the depthwise convolution output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00446">ShapeCalculator.h:446</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="namespacearm__compute_1_1test_1_1validation_xhtml_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">ConvolutionLayer.cpp:183</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_abdb9168800c70e5e2c4c020a3b905738"><div class="ttname"><a href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00494">Validate.h:494</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="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00597">Winograd.cpp:597</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="_n_e_depthwise_convolution_assembly_dispatch_8h_xhtml"><div class="ttname"><a href="_n_e_depthwise_convolution_assembly_dispatch_8h.xhtml">NEDepthwiseConvolutionAssemblyDispatch.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch_xhtml_ac6f25b054a1ebb8fa338c228a41afa06"><div class="ttname"><a href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#ac6f25b054a1ebb8fa338c228a41afa06">arm_compute::NEDepthwiseConvolutionAssemblyDispatch::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier=1, const ActivationLayerInfo &act_info=ActivationLayerInfo(), const Size2D &dilation=Size2D(1, 1))</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEDepthwiseConvolutionAs...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_depthwise_convolution_assembly_dispatch_8cpp_source.xhtml#l00399">NEDepthwiseConvolutionAssemblyDispatch.cpp:399</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="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00466">Error.h:466</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEDepthwiseConvolutionAssemblyDispatch::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_depthwise_convolution_assembly_dispatch_8cpp_source.xhtml#l00542">NEDepthwiseConvolutionAssemblyDispatch.cpp:542</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="classarm__compute_1_1_pad_stride_info_xhtml_a3fea0ce4e6eeee7bf3a511c31b51d44f"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#a3fea0ce4e6eeee7bf3a511c31b51d44f">arm_compute::PadStrideInfo::pad_top</a></div><div class="ttdeci">unsigned int pad_top() const</div><div class="ttdoc">Get the top padding.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00770">Types.h:770</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="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01615">Types.h:1615</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_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00202">Helpers.inl:202</a></div></div> |
| <div class="ttc" id="_c_p_p_2_validate_8h_xhtml_ad2633f3560322e1f8d926949dec1b730"><div class="ttname"><a href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(tensor)</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_validate_8h_source.xhtml#l00071">Validate.h:71</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="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::TensorInfo::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type used for each element of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00265">TensorInfo.h:265</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch_xhtml_a4e3fd833ab574a3299997f4a4faf83df"><div class="ttname"><a href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a4e3fd833ab574a3299997f4a4faf83df">arm_compute::NEDepthwiseConvolutionAssemblyDispatch::NEDepthwiseConvolutionAssemblyDispatch</a></div><div class="ttdeci">NEDepthwiseConvolutionAssemblyDispatch(std::shared_ptr< IMemoryManager > memory_manager=nullptr)</div><div class="ttdoc">Default constructor.</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_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="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</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_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</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="_c_p_p_2_validate_8h_xhtml"><div class="ttname"><a href="_c_p_p_2_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel.</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="_validate_8h_xhtml_a1da797d2762c1cdbb73bfc83136c3a38"><div class="ttname"><a href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00288">Validate.h:288</a></div></div> |
| <div class="ttc" id="namespacearm__compute_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="classarm__compute_1_1_dimensions_xhtml_abb29a685080e999c2a0cb874d2f7bb5a"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#abb29a685080e999c2a0cb874d2f7bb5a">arm_compute::Dimensions::z</a></div><div class="ttdeci">T z() const</div><div class="ttdoc">Alias to access the size of the third dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00091">Dimensions.h:91</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a0b0eb3235749a2909dc5a101afe59a1b"><div class="ttname"><a href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00456">Error.h:456</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_ab988210662dbd3bf32fd563c7dd1bdbf"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">arm_compute::ITensor::buffer</a></div><div class="ttdeci">virtual uint8_t * buffer() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return a pointer to CPU memory.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml_a706fc156bcd4c45441bcaad05884b57d"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">arm_compute::QuantizationInfo::uniform</a></div><div class="ttdeci">UniformQuantizationInfo uniform() const</div><div class="ttdoc">Return per layer quantization info.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00148">QuantizationInfo.h:148</a></div></div> |
| <div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1support_1_1cpp14_xhtml_a1d95a84d1a4610af6a128ad96c907e8b"><div class="ttname"><a href="namespacearm__compute_1_1support_1_1cpp14.xhtml#a1d95a84d1a4610af6a128ad96c907e8b">arm_compute::support::cpp14::make_unique</a></div><div class="ttdeci">_Unique_if< T >::_Single_object make_unique(Args &&... args)</div><div class="ttdoc">Construct a single object and return a unique pointer to it.</div><div class="ttdef"><b>Definition:</b> <a href="_toolchain_support_8h_source.xhtml#l00480">ToolchainSupport.h:480</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_1_pad_stride_info_xhtml_a3860caeeaa259d59ccf69c3aea23f549"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#a3860caeeaa259d59ccf69c3aea23f549">arm_compute::PadStrideInfo::pad_right</a></div><div class="ttdeci">unsigned int pad_right() const</div><div class="ttdoc">Get the right padding.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00765">Types.h:765</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml_af21c7fddee28e9aa0a37c633300db0e0"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml#af21c7fddee28e9aa0a37c633300db0e0">arm_compute::QuantizationInfo::scale</a></div><div class="ttdeci">const std::vector< float > & scale() const</div><div class="ttdoc">Scale vector accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00124">QuantizationInfo.h:124</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch_xhtml_a34b1b5dae78114740544785111bd9a9e"><div class="ttname"><a href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a34b1b5dae78114740544785111bd9a9e">arm_compute::NEDepthwiseConvolutionAssemblyDispatch::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier=1, const ActivationLayerInfo &act_info=ActivationLayerInfo(), const Size2D &dilation=Size2D(1, 1))</div><div class="ttdoc">Initialize the function's source, destination, kernels and border_size.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_depthwise_convolution_assembly_dispatch_8cpp_source.xhtml#l00346">NEDepthwiseConvolutionAssemblyDispatch.cpp:346</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00686">Types.h:686</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_aa459796b5489eca8a9160cb5dcf1a103"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">arm_compute::ITensorInfo::element_size</a></div><div class="ttdeci">virtual size_t element_size() const =0</div><div class="ttdoc">Element size in bytes calculated as data_size() * num_channels()</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="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3a77be8aebd8e00522b32061d46ccdbd"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">arm_compute::test::validation::bias</a></div><div class="ttdeci">CLTensor bias</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00189">ConvolutionLayer.cpp:189</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch_xhtml_a1fb5996aa6bd294a9ef2f7c6ba627578"><div class="ttname"><a href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a1fb5996aa6bd294a9ef2f7c6ba627578">arm_compute::NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported</a></div><div class="ttdeci">static bool is_optimized_supported(const ITensorInfo *input, const ITensorInfo *weights, PadStrideInfo conv_info, unsigned int depth_multiplier=1, const Size2D &dilation=Size2D(1, 1))</div><div class="ttdoc">Check if the optimized kernel can be used for the given kernel sizes and strides.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_depthwise_convolution_assembly_dispatch_8cpp_source.xhtml#l00453">NEDepthwiseConvolutionAssemblyDispatch.cpp:453</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="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_1_1info__helpers_xhtml_adaa2f985265ab514c1a0d5a48703dff1"><div class="ttname"><a href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#adaa2f985265ab514c1a0d5a48703dff1">arm_compute::utils::info_helpers::is_relu6</a></div><div class="ttdeci">bool is_relu6(ActivationLayerInfo activation_info)</div><div class="ttdoc">Checks if activation information correspond to a relu6 activation function.</div><div class="ttdef"><b>Definition:</b> <a href="_info_helpers_8h_source.xhtml#l00053">InfoHelpers.h:53</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">arm_compute::DataType::QSYMM8_PER_CHANNEL</a></div><div class="ttdoc">quantized, symmetric per channel fixed-point 8-bit number</div></div> |
| <div class="ttc" id="_info_helpers_8h_xhtml"><div class="ttname"><a href="_info_helpers_8h.xhtml">InfoHelpers.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_ad0bd5cc32e7e4c0699eccba91e5de397"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#ad0bd5cc32e7e4c0699eccba91e5de397">arm_compute::ITensorInfo::offset_first_element_in_bytes</a></div><div class="ttdeci">virtual size_t offset_first_element_in_bytes() const =0</div><div class="ttdoc">The offset from the beginning of the memory allocation to the first element of the tensor.</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="_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_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_tensor_info_xhtml_a18064e0011c3869d884653e9e7c47b66"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">arm_compute::ITensorInfo::total_size</a></div><div class="ttdeci">virtual size_t total_size() const =0</div><div class="ttdoc">Returns the total size of the tensor in bytes.</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_window_xhtml_a893d17b56b9abc4423ce26e9a24ac5dc"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">arm_compute::Window::DimZ</a></div><div class="ttdeci">static constexpr size_t DimZ</div><div class="ttdoc">Alias for dimension 2 also known as Z dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00047">Window.h:47</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_acd91a85bed01075d0b9384f065f94039"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#acd91a85bed01075d0b9384f065f94039">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a></div><div class="ttdeci">Status calculate_quantized_multiplier_less_than_one(float multiplier, int32_t *quant_multiplier, int32_t *right_shift)</div><div class="ttdoc">Calculate quantized representation of multiplier with value less than one.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00052">AsymmHelpers.cpp:52</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a24954cca5108a24706441fd99a7fb04c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">arm_compute::Tensor::buffer</a></div><div class="ttdeci">uint8_t * buffer() const override</div><div class="ttdoc">Interface to be implemented by the child class to return a pointer to CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00043">Tensor.cpp:43</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ab1806bf0c5a41f674fb9d2dc6af644f5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_1_1info__helpers_xhtml_abb0a3afec6da2c1c38345abccf38ce71"><div class="ttname"><a href="namespacearm__compute_1_1utils_1_1info__helpers.xhtml#abb0a3afec6da2c1c38345abccf38ce71">arm_compute::utils::info_helpers::is_relu</a></div><div class="ttdeci">bool is_relu(ActivationLayerInfo activation_info)</div><div class="ttdoc">Checks if activation information correspond to a relu activation function.</div><div class="ttdef"><b>Definition:</b> <a href="_info_helpers_8h_source.xhtml#l00042">InfoHelpers.h:42</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_ac4a1050be02b20b3f791b9a483f3abe2"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">arm_compute::Dimensions::y</a></div><div class="ttdeci">T y() const</div><div class="ttdoc">Alias to access the size of the second dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00086">Dimensions.h:86</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_i_tensor_info_xhtml_a6b14f175bf5281f57b561e2d4e4b1f1f"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a6b14f175bf5281f57b561e2d4e4b1f1f">arm_compute::ITensorInfo::strides_in_bytes</a></div><div class="ttdeci">virtual const Strides & strides_in_bytes() const =0</div><div class="ttdoc">The strides in bytes for accessing each dimension of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_ac24584a63e484123e3756d1b2a1c9e2f"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">arm_compute::IScheduler::num_threads</a></div><div class="ttdeci">virtual unsigned int num_threads() const =0</div><div class="ttdoc">Returns the number of threads that the SingleThreadScheduler has in his pool.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5f5b6c4337eac9e2e0046ca2304d80dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">ArithmeticAddition.cpp:138</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="_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_depthwise_convolution_assembly_dispatch_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEDepthwiseConvolutionAssemblyDispatch::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_depthwise_convolution_assembly_dispatch_8cpp_source.xhtml#l00511">NEDepthwiseConvolutionAssemblyDispatch.cpp:511</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml_ac49df0924d183da42cffc04cf9aba1f8"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#ac49df0924d183da42cffc04cf9aba1f8">arm_compute::PadStrideInfo::pad_bottom</a></div><div class="ttdeci">unsigned int pad_bottom() const</div><div class="ttdoc">Get the bottom padding.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00775">Types.h:775</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape & tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00261">TensorInfo.h:261</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00075">Types.h:75</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml_a7144874ab401f5c4e249a1115dfb5166"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml#a7144874ab401f5c4e249a1115dfb5166">arm_compute::PadStrideInfo::pad_left</a></div><div class="ttdeci">unsigned int pad_left() const</div><div class="ttdoc">Get the left padding.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00760">Types.h:760</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::DataType::S8</a></div><div class="ttdoc">signed 8-bit number</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_af5982a092e9eb743fce2d6392bdd8897"><div class="ttname"><a href="namespacearm__compute.xhtml#af5982a092e9eb743fce2d6392bdd8897">arm_compute::is_data_type_float</a></div><div class="ttdeci">bool is_data_type_float(DataType dt)</div><div class="ttdoc">Check if a given data type is of floating point type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01097">Utils.h:1097</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch_xhtml_a74a504608c72ef3a540729f2b39fe7c9"><div class="ttname"><a href="classarm__compute_1_1_n_e_depthwise_convolution_assembly_dispatch.xhtml#a74a504608c72ef3a540729f2b39fe7c9">arm_compute::NEDepthwiseConvolutionAssemblyDispatch::~NEDepthwiseConvolutionAssemblyDispatch</a></div><div class="ttdeci">~NEDepthwiseConvolutionAssemblyDispatch()</div><div class="ttdoc">Default destructor.</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_aa6d4f0b9fedd979c5b768f9b34fda9f6"><div class="ttname"><a href="namespacearm__compute.xhtml#aa6d4f0b9fedd979c5b768f9b34fda9f6">arm_compute::calculate_same_pad</a></div><div class="ttdeci">PadStrideInfo calculate_same_pad(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info, DataLayout data_layout=DataLayout::NCHW, const Size2D &dilation=Size2D(1u, 1u), const DimensionRoundingType &rounding_type=DimensionRoundingType::FLOOR)</div><div class="ttdoc">Calculate padding requirements in case of SAME padding.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00340">Utils.cpp:340</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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