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<div class="title">CLPadLayer.cpp</div> </div>
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<a href="_c_l_pad_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2018-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<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>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<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>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_c_l_pad_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLPadLayer.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_i_c_l_tensor_8h.xhtml">arm_compute/core/CL/ICLTensor.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_shape_calculator_8h.xhtml">arm_compute/core/utils/misc/ShapeCalculator.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;{</div><div class="line"><a name="l00033"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_pad_layer.xhtml#ae9f904daf8cd24be8972f9d694d02390"> 33</a></span>&#160;<a class="code" href="classarm__compute_1_1_c_l_pad_layer.xhtml#ae9f904daf8cd24be8972f9d694d02390">CLPadLayer::CLPadLayer</a>()</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; : _copy_kernel(), _mode(), _padding(), _memset_kernel(), _num_dimensions(0), _slice_functions(), _concat_functions(), _slice_results(), _concat_results()</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;}</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;<span class="keywordtype">void</span> CLPadLayer::configure_constant_mode(<a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> constant_value)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;{</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="comment">// Set the pages of the output to the constant_value.</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; _memset_kernel.<a class="code" href="classarm__compute_1_1_c_l_memset_kernel.xhtml#a8842f3a8e50c91b74a0b0549ac8fa489">configure</a>(output, constant_value);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="comment">// Fill out padding list with zeroes.</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a> padding_extended = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>.size(); i &lt; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a>; i++)</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; padding_extended.emplace_back(<a class="code" href="namespacearm__compute.xhtml#a669b5d3c5994f9ae3be31df9a1014297">PaddingInfo</a>{ 0, 0 });</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; }</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <span class="comment">// Create a window within the output tensor where the input will be copied.</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; Window copy_window = Window();</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <span class="keywordflow">for</span>(uint32_t i = 0; i &lt; output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>(); ++i)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(i), 1));</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; }</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="comment">// Copy the input to the output, leaving the padding filled with the constant_value.</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; _copy_kernel.<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a71e93e8e995e940376c12cfd1b0a5538">configure</a>(input, output, <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a>(), &amp;copy_window);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;}</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;<span class="keywordtype">void</span> CLPadLayer::configure_reflect_symmetric_mode(ICLTensor *input, ICLTensor *output)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;{</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; int64_t last_padding_dimension = _padding.size() - 1;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="comment">// Reflecting can be performed by effectively unfolding the input as follows:</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="comment">// For each dimension starting at DimX:</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="comment">// Create a before and after slice, which values depend on the selected padding mode</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="comment">// Concatenate the before and after padding with the tensor to be padded</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="comment">// Two strided slice functions will be required for each dimension padded as well as a</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="comment">// concatenate function and the tensors to hold the temporary results.</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; _slice_functions.resize(2 * _num_dimensions);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; _slice_results.resize(2 * _num_dimensions);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; _concat_functions.resize(_num_dimensions);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; _concat_results.resize(_num_dimensions - 1);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; Coordinates starts_before{};</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; Coordinates ends_before{};</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; Coordinates starts_after{};</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; Coordinates ends_after{};</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; Coordinates strides{};</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; ICLTensor *prev = input;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; <span class="keywordflow">for</span>(uint32_t i = 0; i &lt; _num_dimensions; ++i)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <span class="comment">// Values in strides from the previous dimensions need to be set to 1 to avoid reversing again.</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <span class="keywordflow">if</span>(i &gt; 0)</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; {</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; strides.set(i - 1, 1);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <span class="keywordflow">if</span>(_padding[i].first &gt; 0 || _padding[i].second &gt; 0)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="comment">// Set the starts, ends, and strides values for the current dimension.</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="comment">// Due to the bit masks passed to strided slice, the values below the current dimension in</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <span class="comment">// starts and ends will be ignored so do not need to be modified.</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="keywordflow">if</span>(_mode == <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91ae4f6a05f82ed398f984f4bc1a55838df">PaddingMode::REFLECT</a>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; starts_before.set(i, _padding[i].first);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; ends_before.set(i, 0);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; starts_after.set(i, input-&gt;info()-&gt;dimension(i) - 2);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; ends_after.set(i, input-&gt;info()-&gt;dimension(i) - _padding[i].second - 2);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; strides.set(i, -1);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; }</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; starts_before.set(i, _padding[i].first - 1);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; ends_before.set(i, -1);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; starts_after.set(i, input-&gt;info()-&gt;dimension(i) - 1);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; ends_after.set(i, input-&gt;info()-&gt;dimension(i) - _padding[i].second - 1);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; strides.set(i, -1);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="comment">// Strided slice wraps negative indexes around to the end of the range,</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="comment">// instead this should indicate use of the full range and so the bit mask will be modified.</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <span class="keyword">const</span> int32_t begin_mask_before = starts_before[i] &lt; 0 ? ~0 : ~(1u &lt;&lt; i);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="keyword">const</span> int32_t end_mask_before = ends_before[i] &lt; 0 ? ~0 : ~(1u &lt;&lt; i);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <span class="keyword">const</span> int32_t begin_mask_after = starts_after[i] &lt; 0 ? ~0 : ~(1u &lt;&lt; i);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keyword">const</span> int32_t end_mask_after = ends_after[i] &lt; 0 ? ~0 : ~(1u &lt;&lt; i);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <span class="comment">// Reflect the input values for the padding before and after the input.</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; std::vector&lt;ICLTensor *&gt; concat_vector;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keywordflow">if</span>(_padding[i].first &gt; 0)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <span class="keywordflow">if</span>(i &lt; prev-&gt;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>()-&gt;num_dimensions())</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; _slice_functions[2 * i].configure(prev, &amp;_slice_results[2 * i], starts_before, ends_before, strides, begin_mask_before, end_mask_before);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; concat_vector.push_back(&amp;_slice_results[2 * i]);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="comment">// Performing the slice is unnecessary if the result would simply be a copy of the tensor.</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; concat_vector.push_back(prev);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; concat_vector.push_back(prev);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <span class="keywordflow">if</span>(_padding[i].second &gt; 0)</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <span class="keywordflow">if</span>(i &lt; prev-&gt;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>()-&gt;num_dimensions())</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; {</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; _slice_functions[2 * i + 1].configure(prev, &amp;_slice_results[2 * i + 1], starts_after, ends_after, strides, begin_mask_after, end_mask_after);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; concat_vector.push_back(&amp;_slice_results[2 * i + 1]);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="comment">// Performing the slice is unnecessary if the result would simply be a copy of the tensor.</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; concat_vector.push_back(prev);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; }</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <span class="comment">// Concatenate the padding before and after with the input.</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; ICLTensor *out = (static_cast&lt;int32_t&gt;(i) == last_padding_dimension) ? output : &amp;_concat_results[i];</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; _concat_functions[i].configure(concat_vector, out, i);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; prev = out;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; }</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="keywordflow">for</span>(uint32_t i = 0; i &lt; _num_dimensions; ++i)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; {</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="keywordflow">if</span>((static_cast&lt;int32_t&gt;(i) != last_padding_dimension))</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; _concat_results[i].allocator()-&gt;allocate();</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; _slice_results[2 * i].allocator()-&gt;allocate();</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; _slice_results[2 * i + 1].allocator()-&gt;allocate();</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; }</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160;}</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160;</div><div class="line"><a name="l00164"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_pad_layer.xhtml#ad6041ae1c7d7fd4ba3231128586362b0"> 164</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_pad_layer.xhtml#ad6041ae1c7d7fd4ba3231128586362b0">CLPadLayer::configure</a>(<a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>, <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> constant_value, <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91">PaddingMode</a> mode)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160;{</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_c_l_pad_layer.xhtml#a5ab39485b5d0b51df3472895ed0c00a2">validate</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-&gt;<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#a735a025fce26c1ef147b54426df18181">padding</a>, constant_value, mode));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; _padding = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; _mode = mode;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> padded_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4e7f3187350db69156c1026860ace4e5">misc::shape_calculator::compute_padded_shape</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), _padding);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()-&gt;set_tensor_shape(padded_shape));</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; <span class="comment">// Find the last dimension requiring padding so that it is known when to write to output and whether any padding is applied.</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; int64_t last_padding_dimension = _padding.size() - 1;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; <span class="keywordflow">for</span>(; last_padding_dimension &gt;= 0; --last_padding_dimension)</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <span class="keywordflow">if</span>(_padding[last_padding_dimension].first &gt; 0 || _padding[last_padding_dimension].second &gt; 0)</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; {</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; _num_dimensions = last_padding_dimension + 1;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <span class="keywordflow">if</span>(_num_dimensions &gt; 0)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; <span class="keywordflow">switch</span>(_mode)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; {</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">PaddingMode::CONSTANT</a>:</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; configure_constant_mode(input, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>, constant_value);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91ae4f6a05f82ed398f984f4bc1a55838df">PaddingMode::REFLECT</a>:</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a161b3d9016563aba9ac190fc02ada9bb">PaddingMode::SYMMETRIC</a>:</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; configure_reflect_symmetric_mode(input, output);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; }</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">&quot;Padding mode not supported.&quot;</span>);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; }</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; <span class="comment">// Copy the input to the whole output if no padding is applied</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; _copy_kernel.<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a71e93e8e995e940376c12cfd1b0a5538">configure</a>(input, output);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; }</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160;}</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160;</div><div class="line"><a name="l00211"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_pad_layer.xhtml#a5ab39485b5d0b51df3472895ed0c00a2"> 211</a></span>&#160;<a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_c_l_pad_layer.xhtml#a5ab39485b5d0b51df3472895ed0c00a2">CLPadLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>, <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> constant_value, <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91">PaddingMode</a> mode)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160;{</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; <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#a735a025fce26c1ef147b54426df18181">padding</a>.size() &gt; input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>());</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160;</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> padded_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4e7f3187350db69156c1026860ace4e5">misc::shape_calculator::compute_padded_shape</a>(input-&gt;<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#a735a025fce26c1ef147b54426df18181">padding</a>);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <span class="comment">// Use CLCopyKernel and CLMemsetKernel to validate all padding modes as this includes all of the shape and info validation.</span></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a> padding_extended = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>.size(); i &lt; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a>; i++)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; padding_extended.emplace_back(<a class="code" href="namespacearm__compute.xhtml#a669b5d3c5994f9ae3be31df9a1014297">PaddingInfo</a>{ 0, 0 });</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; }</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> copy_window = <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a>();</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; <span class="keywordflow">for</span>(uint32_t i = 0; i &lt; padded_shape.num_dimensions(); ++i)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; {</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; copy_window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(i, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(padding_extended[i].first, padding_extended[i].first + input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(i), 1));</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; }</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; <span class="keywordflow">if</span>(output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>() &gt; 0)</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), padded_shape);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(output, input);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a27b2f705eda7702c5835196e160b111f">CLCopyKernel::validate</a>(input, output, <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a>(), &amp;copy_window));</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_memset_kernel.xhtml#a471da770dae686275f4e1c527042080f">CLMemsetKernel::validate</a>(output, constant_value));</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a27b2f705eda7702c5835196e160b111f">CLCopyKernel::validate</a>(input, &amp;input-&gt;<a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()-&gt;set_tensor_shape(padded_shape), <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a>(), &amp;copy_window));</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_memset_kernel.xhtml#a471da770dae686275f4e1c527042080f">CLMemsetKernel::validate</a>(&amp;input-&gt;<a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()-&gt;set_tensor_shape(padded_shape), constant_value));</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; <span class="keywordflow">switch</span>(mode)</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">PaddingMode::CONSTANT</a>:</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; {</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91ae4f6a05f82ed398f984f4bc1a55838df">PaddingMode::REFLECT</a>:</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a161b3d9016563aba9ac190fc02ada9bb">PaddingMode::SYMMETRIC</a>:</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <span class="keywordflow">for</span>(uint32_t i = 0; i &lt; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>.size(); ++i)</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; {</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <span class="keywordflow">if</span>(mode == <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91ae4f6a05f82ed398f984f4bc1a55838df">PaddingMode::REFLECT</a>)</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; <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#a735a025fce26c1ef147b54426df18181">padding</a>[i].first &gt;= input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(i));</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <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#a735a025fce26c1ef147b54426df18181">padding</a>[i].second &gt;= input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(i));</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; <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#a735a025fce26c1ef147b54426df18181">padding</a>[i].first &gt; input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(i));</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <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#a735a025fce26c1ef147b54426df18181">padding</a>[i].second &gt; input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(i));</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; }</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; }</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; }</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">&quot;Invalid mode&quot;</span>);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; }</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; }</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160;}</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160;</div><div class="line"><a name="l00274"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_pad_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 274</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_pad_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">CLPadLayer::run</a>()</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160;{</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; <span class="keywordflow">if</span>(_num_dimensions &gt; 0)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; {</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <span class="keywordflow">switch</span>(_mode)</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; {</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">PaddingMode::CONSTANT</a>:</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; {</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_memset_kernel, <span class="keyword">false</span>);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_copy_kernel, <span class="keyword">true</span>);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; }</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91ae4f6a05f82ed398f984f4bc1a55838df">PaddingMode::REFLECT</a>:</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a161b3d9016563aba9ac190fc02ada9bb">PaddingMode::SYMMETRIC</a>:</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; {</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; <span class="keywordflow">for</span>(uint32_t i = 0; i &lt; _num_dimensions; ++i)</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; <span class="keywordflow">if</span>(_padding[i].first &gt; 0 || _padding[i].second &gt; 0)</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; {</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; <span class="keywordflow">if</span>(_padding[i].first &gt; 0 &amp;&amp; _slice_results[2 * i].<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>()-&gt;total_size() &gt; 0)</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; {</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; _slice_functions[2 * i].run();</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; }</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <span class="keywordflow">if</span>(_padding[i].second &gt; 0 &amp;&amp; _slice_results[2 * i + 1].<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>()-&gt;total_size() &gt; 0)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; {</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; _slice_functions[2 * i + 1].run();</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; }</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ad55f80ed3cd8b6c4f247763b747016af">sync</a>();</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; _concat_functions[i].run();</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ad55f80ed3cd8b6c4f247763b747016af">sync</a>();</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; }</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; }</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; }</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; <span class="keywordflow">default</span>:</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">&quot;Padding mode not supported.&quot;</span>);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; }</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; }</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; {</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_copy_kernel, <span class="keyword">true</span>);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; }</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160;}</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="classarm__compute_1_1_c_l_memset_kernel_xhtml_a471da770dae686275f4e1c527042080f"><div class="ttname"><a href="classarm__compute_1_1_c_l_memset_kernel.xhtml#a471da770dae686275f4e1c527042080f">arm_compute::CLMemsetKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *tensor, const PixelValue &amp;constant_value, Window *window=nullptr)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLMemsetKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_memset_kernel_8cpp_source.xhtml#l00082">CLMemsetKernel.cpp:82</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00261">Error.h:261</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div>
<div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml">arm_compute::PixelValue</a></div><div class="ttdoc">Class describing the value of a pixel for any image format.</div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00034">PixelValue.h:34</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="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ac1a1b012674e0f1de071a611391828ad"><div class="ttname"><a href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">arm_compute::PaddingList</a></div><div class="ttdeci">std::vector&lt; PaddingInfo &gt; PaddingList</div><div class="ttdoc">List of padding information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00445">Types.h:445</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></div></div>
<div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00193">Error.h:193</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_copy_kernel_xhtml_a71e93e8e995e940376c12cfd1b0a5538"><div class="ttname"><a href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a71e93e8e995e940376c12cfd1b0a5538">arm_compute::CLCopyKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const PaddingList &amp;padding=PaddingList(), Window *output_window=nullptr)</div><div class="ttdoc">Initialize the kernel's input, output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_copy_kernel_8cpp_source.xhtml#l00157">CLCopyKernel.cpp:157</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#l00327">Error.h:327</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image's dimensions with a start, end and step.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00075">Window.h:75</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="classarm__compute_1_1_c_l_pad_layer_xhtml_a5ab39485b5d0b51df3472895ed0c00a2"><div class="ttname"><a href="classarm__compute_1_1_c_l_pad_layer.xhtml#a5ab39485b5d0b51df3472895ed0c00a2">arm_compute::CLPadLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &amp;padding, PixelValue constant_value=PixelValue(), PaddingMode mode=PaddingMode::CONSTANT)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLPadLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pad_layer_8cpp_source.xhtml#l00211">CLPadLayer.cpp:211</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91a161b3d9016563aba9ac190fc02ada9bb"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a161b3d9016563aba9ac190fc02ada9bb">arm_compute::PaddingMode::SYMMETRIC</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) 2017-2018 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 &amp;info, const TensorShape &amp;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#l00201">Helpers.inl:201</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape &amp; tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91">arm_compute::PaddingMode</a></div><div class="ttdeci">PaddingMode</div><div class="ttdoc">Padding mode to use for PadLayer.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00155">Types.h:155</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_pad_layer_xhtml_ad6041ae1c7d7fd4ba3231128586362b0"><div class="ttname"><a href="classarm__compute_1_1_c_l_pad_layer.xhtml#ad6041ae1c7d7fd4ba3231128586362b0">arm_compute::CLPadLayer::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output, const PaddingList &amp;padding, PixelValue constant_value=PixelValue(), PaddingMode mode=PaddingMode::CONSTANT)</div><div class="ttdoc">Initialize the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pad_layer_8cpp_source.xhtml#l00164">CLPadLayer.cpp:164</a></div></div>
<div class="ttc" id="_validate_8h_xhtml_a1da797d2762c1cdbb73bfc83136c3a38"><div class="ttname"><a href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00288">Validate.h:288</a></div></div>
<div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr&lt; T &gt; clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_copy_kernel_xhtml_a27b2f705eda7702c5835196e160b111f"><div class="ttname"><a href="classarm__compute_1_1_c_l_copy_kernel.xhtml#a27b2f705eda7702c5835196e160b111f">arm_compute::CLCopyKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &amp;padding=PaddingList(), Window *output_window=nullptr)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLCopyKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_copy_kernel_8cpp_source.xhtml#l00230">CLCopyKernel.cpp:230</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &amp;dim)</div><div class="ttdoc">Set the values of a given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00048">Window.inl:48</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a4e7f3187350db69156c1026860ace4e5"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4e7f3187350db69156c1026860ace4e5">arm_compute::misc::shape_calculator::compute_padded_shape</a></div><div class="ttdeci">TensorShape compute_padded_shape(const TensorShape &amp;input_shape, const PaddingList &amp;padding)</div><div class="ttdoc">Calculate the padded shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01149">ShapeCalculator.h:1149</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_pad_layer_xhtml_ae9f904daf8cd24be8972f9d694d02390"><div class="ttname"><a href="classarm__compute_1_1_c_l_pad_layer.xhtml#ae9f904daf8cd24be8972f9d694d02390">arm_compute::CLPadLayer::CLPadLayer</a></div><div class="ttdeci">CLPadLayer()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pad_layer_8cpp_source.xhtml#l00033">CLPadLayer.cpp:33</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ae1a643e517f50bf0392fb6516dd7cf67"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">arm_compute::CLScheduler::enqueue</a></div><div class="ttdeci">void enqueue(ICLKernel &amp;kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler.cpp:95</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ad55f80ed3cd8b6c4f247763b747016af"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ad55f80ed3cd8b6c4f247763b747016af">arm_compute::CLScheduler::sync</a></div><div class="ttdeci">void sync()</div><div class="ttdoc">Blocks until all commands in the associated command queue have finished.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00151">CLScheduler.h:151</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_tensor.xhtml">arm_compute::ICLTensor</a></div><div class="ttdoc">Interface for OpenCL tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_tensor_8h_source.xhtml#l00042">ICLTensor.h:42</a></div></div>
<div class="ttc" id="_i_c_l_tensor_8h_xhtml"><div class="ttname"><a href="_i_c_l_tensor_8h.xhtml">ICLTensor.h</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="namespacearm__compute_xhtml_a669b5d3c5994f9ae3be31df9a1014297"><div class="ttname"><a href="namespacearm__compute.xhtml#a669b5d3c5994f9ae3be31df9a1014297">arm_compute::PaddingInfo</a></div><div class="ttdeci">std::pair&lt; uint32_t, uint32_t &gt; PaddingInfo</div><div class="ttdoc">Padding information as a pair of unsigned int start/end.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00442">Types.h:442</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">arm_compute::PaddingMode::CONSTANT</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91ae4f6a05f82ed398f984f4bc1a55838df"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91ae4f6a05f82ed398f984f4bc1a55838df">arm_compute::PaddingMode::REFLECT</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_memset_kernel_xhtml_a8842f3a8e50c91b74a0b0549ac8fa489"><div class="ttname"><a href="classarm__compute_1_1_c_l_memset_kernel.xhtml#a8842f3a8e50c91b74a0b0549ac8fa489">arm_compute::CLMemsetKernel::configure</a></div><div class="ttdeci">void configure(ICLTensor *tensor, const PixelValue &amp;constant_value, Window *window=nullptr)</div><div class="ttdoc">Initialise the kernel's tensor and filling value.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_memset_kernel_8cpp_source.xhtml#l00042">CLMemsetKernel.cpp:42</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_pad_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_pad_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLPadLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_pad_layer_8cpp_source.xhtml#l00274">CLPadLayer.cpp:274</a></div></div>
<div class="ttc" id="_c_l_pad_layer_8h_xhtml"><div class="ttname"><a href="_c_l_pad_layer_8h.xhtml">CLPadLayer.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a1b67d5b720119d50faa286c774579ecc"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">arm_compute::Dimensions&lt; size_t &gt;::num_max_dimensions</a></div><div class="ttdeci">static constexpr size_t num_max_dimensions</div><div class="ttdoc">Number of dimensions the tensor has.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00045">Dimensions.h:45</a></div></div>
<div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a735a025fce26c1ef147b54426df18181"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">arm_compute::test::validation::padding</a></div><div class="ttdeci">const PaddingSize padding</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00113">AbsoluteDifference.cpp:113</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div>
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