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| <a href="_g_c_direct_convolution_layer_kernel_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_g_c_direct_convolution_layer_kernel_8h.xhtml">arm_compute/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_access_window_static_8h.xhtml">arm_compute/core/AccessWindowStatic.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_g_c_helpers_8h.xhtml">arm_compute/core/GLES_COMPUTE/GCHelpers.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_g_c_kernel_library_8h.xhtml">arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_i_g_c_tensor_8h.xhtml">arm_compute/core/GLES_COMPUTE/IGCTensor.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_i_access_window_8h.xhtml">arm_compute/core/IAccessWindow.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="_validate_8h.xhtml">arm_compute/core/Validate.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">template</span> <<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_size></div><div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#ab55e3264789da35ae1297fefa4efed7c"> 41</a></span> <a class="code" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#ab55e3264789da35ae1297fefa4efed7c">GCDirectConvolutionLayerKernel<kernel_size>::GCDirectConvolutionLayerKernel</a>()</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  : _input(nullptr), _bias(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_x(0), _conv_pad_y(0), _lws(gles::NDRange(1U, 1U, 1U))</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> }</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">template</span> <<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_size></div><div class="line"><a name="l00047"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#a423f9a45a52983b4de5e2b347f4369c7"> 47</a></span> <a class="code" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a> <a class="code" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#a423f9a45a52983b4de5e2b347f4369c7">GCDirectConvolutionLayerKernel<kernel_size>::border_size</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword"></span>{</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">return</span> _border_size;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> }</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">template</span> <<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_size></div><div class="line"><a name="l00053"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#ab904c60f24f8f8f44e24d71f337c86a9"> 53</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#ab904c60f24f8f8f44e24d71f337c86a9">GCDirectConvolutionLayerKernel<kernel_size>::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>, <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</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>, <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="l00055"></a><span class="lineno"> 55</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <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="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(2) != 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2));</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1));</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b">num_dimensions</a>() > 4);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>((kernel_size == 3 && std::get<0>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride()) > 2), <span class="stringliteral">"Strides larger than 2 not supported in 3x3 direct convolution!"</span>);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(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#a8813441b655b97c00139c6a5a6390e97">dimension</a>(0));</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled() && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation() != <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a> && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation() != <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">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="l00065"></a><span class="lineno"> 65</span>  {</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// FIXME: Bug in framework, workaround it in tests currently.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">//ARM_COMPUTE_ERROR_ON(bias->info()->dimension(0) != weights->info()->dimension(3));</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</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#a13622133d9b41900a6a3e8f89e59a78b">num_dimensions</a>() > 1);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  }</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="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> owidth = 0;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> oheight = 0;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::tie(owidth, oheight) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0), 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1), kernel_size, kernel_size, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, owidth);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, oheight);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(3));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">// Output auto inizialitation if not yet initialized</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, 1, 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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</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>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="_validate_8h.xhtml#ad9fd47433ba6091668c207e21dd6385f">ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS</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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, output);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.padding_is_symmetric());</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  _conv_stride_x = std::get<0>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride());</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  _conv_stride_y = std::get<1>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride());</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  _conv_pad_x = std::get<0>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad());</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  _conv_pad_y = std::get<1>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad());</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  _input = input;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  _weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  _output = output;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  _bias = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  _border_size = <a class="code" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a>(_conv_pad_y, _conv_pad_x);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  std::set<std::string> options;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  options.emplace(<span class="stringliteral">"#define LOCAL_SIZE_X "</span> + <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">support::cpp11::to_string</a>(_lws[0]));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  options.emplace(<span class="stringliteral">"#define LOCAL_SIZE_Y "</span> + <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">support::cpp11::to_string</a>(_lws[1]));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  options.emplace(<span class="stringliteral">"#define LOCAL_SIZE_Z "</span> + <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">support::cpp11::to_string</a>(_lws[2]));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  options.emplace(<span class="stringliteral">"#define STRIDE_X "</span> + <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">support::cpp11::to_string</a>(_conv_stride_x));</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  options.emplace(<span class="stringliteral">"#define STRIDE_Y "</span> + <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">support::cpp11::to_string</a>(_conv_stride_y));</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>  std::string dt_name = (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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>) ? <span class="stringliteral">"DATA_TYPE_FP32"</span> : <span class="stringliteral">"DATA_TYPE_FP16"</span>;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  options.emplace((<span class="stringliteral">"#define "</span> + dt_name));</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="comment">// Activation information in case of a fused activation</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled())</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  {</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  options.emplace(<span class="stringliteral">"#define FUSED_ACTIVATION"</span>);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  options.emplace((<span class="stringliteral">"#define "</span> + <a class="code" href="namespacearm__compute.xhtml#a635f1895d94050329b7da12850d1a056">string_from_activation_func</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation())));</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  options.emplace((<span class="stringliteral">"#define ACT_OP "</span> + <a class="code" href="namespacearm__compute.xhtml#a0a7bb17a0a0414a7162f635776a02eb5">lower_string</a>(<a class="code" href="namespacearm__compute.xhtml#a635f1895d94050329b7da12850d1a056">string_from_activation_func</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation())) + <span class="stringliteral">"_op"</span>));</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  options.emplace((<span class="stringliteral">"#define A_VAL "</span> + <a class="code" href="namespacearm__compute.xhtml#a9cd394c15b73f79ca1d98f5328064be2">float_to_string_with_full_precision</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.a())));</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  options.emplace((<span class="stringliteral">"#define B_VAL "</span> + <a class="code" href="namespacearm__compute.xhtml#a9cd394c15b73f79ca1d98f5328064be2">float_to_string_with_full_precision</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.b())));</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_elems_read_per_iteration_x = kernel_size * _conv_stride_x;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_elems_read_per_iteration_y = 1;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_elems_written_per_iteration_x = 1;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_elems_written_per_iteration_y = 1;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_elems_written_per_iteration_z = 1;</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>  <span class="keywordflow">if</span>(kernel_size == 3)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">if</span>((_conv_stride_x == 1) && (_conv_stride_y == 1))</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">switch</span>(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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="comment">// TODO(APPBROWSER-299): Choose the most optimal path and remove others.</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="preprocessor">#define PROCESS_4X_3Y_1Z</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> <span class="preprocessor">#if defined(PROCESS_8X_3Y_1Z)</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  options.emplace(<span class="stringliteral">"#define PROCESS_8X_3Y_1Z"</span>);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  num_elems_read_per_iteration_x = 16;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  num_elems_read_per_iteration_y = 5;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  num_elems_written_per_iteration_x = 8;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  num_elems_written_per_iteration_y = 3;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="preprocessor">#elif defined(PROCESS_4X_3Y_1Z)</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_3Y_1Z"</span>);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  num_elems_read_per_iteration_y = 5;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  num_elems_written_per_iteration_y = 3;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="preprocessor">#elif defined(PROCESS_4X_4Y_1Z)</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_4Y_1Z"</span>);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  num_elems_read_per_iteration_y = 6;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  num_elems_written_per_iteration_y = 4;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="preprocessor">#elif defined(PROCESS_4X_3Y_2Z)</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_3Y_2Z"</span>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  num_elems_read_per_iteration_y = 5;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  num_elems_written_per_iteration_y = 3;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  num_elems_written_per_iteration_z = 2;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> <span class="preprocessor">#endif </span><span class="comment">/* PROCESS_nX_nY_nZ */</span><span class="preprocessor"></span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="preprocessor">#undef PROCESS_8X_3Y_1Z</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="preprocessor">#undef PROCESS_4X_3Y_1Z</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="preprocessor">#undef PROCESS_4X_4Y_1Z</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="preprocessor">#undef PROCESS_4X_3Y_2Z</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_3Y_1Z"</span>);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  num_elems_read_per_iteration_y = 5;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  num_elems_written_per_iteration_y = 3;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">break</span>;</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">default</span>:</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Current data type is not supported"</span>);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">break</span>;</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>  }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// FIXME: Just keep one in release</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  {</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">switch</span>(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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_1Y_1Z"</span>);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">break</span>;</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">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="comment">// TODO(APPBROWSER-299): Choose the most optimal path and remove others.</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="preprocessor">#define PROCESS_4X_1Y_1Z</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="preprocessor">#if defined(PROCESS_1X_1Y_1Z)</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  options.emplace(<span class="stringliteral">"#define PROCESS_1X_1Y_1Z"</span>);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  num_elems_read_per_iteration_x = 3;</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  num_elems_written_per_iteration_x = 1;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="preprocessor">#elif defined(PROCESS_4X_1Y_1Z)</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_1Y_1Z"</span>);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> <span class="preprocessor">#elif defined(PROCESS_8X_1Y_1Z)</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  options.emplace(<span class="stringliteral">"#define PROCESS_8X_1Y_1Z"</span>);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  num_elems_read_per_iteration_x = 12;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  num_elems_written_per_iteration_x = 8;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="preprocessor">#else </span><span class="comment">/* PROCESS_nX_nY_nZ */</span><span class="preprocessor"></span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="preprocessor">#error Have to declare how many elements to process in one thread.</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> <span class="preprocessor">#endif </span><span class="comment">/* PROCESS_nX_nY_nZ */</span><span class="preprocessor"></span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="preprocessor">#undef PROCESS_1X_1Y_1Z</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> <span class="preprocessor">#undef PROCESS_4X_1Y_1Z</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> <span class="preprocessor">#undef PROCESS_8X_1Y_1Z</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Current data type is not supported"</span>);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">break</span>;</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>  }</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == 1)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  {</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(2) % 2 == 0)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  options.emplace(<span class="stringliteral">"#define WEIGHTS_OPTIMIZATION"</span>);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">switch</span>(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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="preprocessor">#define PROCESS_8X_2Y_1Z</span></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="preprocessor">#if defined(PROCESS_4X_1Y_1Z)</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_1Y_1Z"</span>);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  num_elems_read_per_iteration_x = 4;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="preprocessor">#elif defined(PROCESS_4X_2Y_1Z)</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_2Y_1Z"</span>);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  num_elems_read_per_iteration_x = 4;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  num_elems_read_per_iteration_y = 2;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  num_elems_written_per_iteration_y = 2;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="preprocessor">#elif defined(PROCESS_4X_3Y_1Z)</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_3Y_1Z"</span>);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  num_elems_read_per_iteration_x = 4;</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  num_elems_read_per_iteration_y = 3;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  num_elems_written_per_iteration_y = 3;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="preprocessor">#elif defined(PROCESS_4X_4Y_1Z)</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_4Y_1Z"</span>);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  num_elems_read_per_iteration_x = 4;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  num_elems_read_per_iteration_y = 4;</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  num_elems_written_per_iteration_y = 4;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="preprocessor">#elif defined(PROCESS_4X_2Y_2Z)</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(4) % 2) == 1, <span class="stringliteral">"Current 'weights->info()->dimension(4) % 2) == 1' is not supported"</span>);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_2Y_2Z"</span>);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  num_elems_read_per_iteration_x = 4;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  num_elems_read_per_iteration_y = 2;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  num_elems_written_per_iteration_x = 4;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  num_elems_written_per_iteration_y = 2;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  num_elems_written_per_iteration_z = 2;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="preprocessor">#elif defined(PROCESS_8X_1Y_1Z)</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  options.emplace(<span class="stringliteral">"#define PROCESS_8X_1Y_1Z"</span>);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  num_elems_written_per_iteration_x = 8;</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <span class="preprocessor">#elif defined(PROCESS_8X_2Y_1Z)</span></div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  options.emplace(<span class="stringliteral">"#define PROCESS_8X_2Y_1Z"</span>);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  num_elems_read_per_iteration_y = 2;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  num_elems_written_per_iteration_x = 8;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  num_elems_written_per_iteration_y = 2;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="preprocessor">#else </span><span class="comment">/* PROCESS_4X_1Y_1Z */</span><span class="preprocessor"></span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="preprocessor">#error Have to declare how many elements to process in one thread.</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <span class="preprocessor">#endif </span><span class="comment">/* PROCESS_4X_1Y_1Z */</span><span class="preprocessor"></span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> <span class="preprocessor">#undef PROCESS_4X_1Y_1Z</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> <span class="preprocessor">#undef PROCESS_4X_2Y_1Z</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> <span class="preprocessor">#undef PROCESS_4X_3Y_1Z</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> <span class="preprocessor">#undef PROCESS_4X_4Y_1Z</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> <span class="preprocessor">#undef PROCESS_4X_2Y_2Z</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> <span class="preprocessor">#undef PROCESS_8X_1Y_1Z</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> <span class="preprocessor">#undef PROCESS_8X_2Y_1Z</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keywordflow">break</span>;</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  num_elems_read_per_iteration_x = 1;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  num_elems_written_per_iteration_x = 1;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">break</span>;</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">default</span>:</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  }</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  }</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == 5)</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  {</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordflow">switch</span>(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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  {</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  options.emplace(<span class="stringliteral">"#define PROCESS_4X_1Y_1Z"</span>);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  num_elems_read_per_iteration_x = 8;</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  num_elems_written_per_iteration_x = 4;</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">default</span>:</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">break</span>;</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>  }</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  {</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  }</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> </div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">if</span>(_bias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  options.emplace(<span class="stringliteral">"#define BIAS"</span>);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  }</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>  std::stringstream kernel_name;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  kernel_name << <span class="stringliteral">"direct_convolution"</span> << kernel_size << <span class="stringliteral">"x"</span> << kernel_size;</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>  _kernel = static_cast<GCKernel>(<a class="code" href="classarm__compute_1_1_g_c_kernel_library.xhtml#a677e1b4dae70aaa7a18e9a8f1a4692e8">GCKernelLibrary::get</a>().<a class="code" href="namespacearm__compute.xhtml#abc72c95941485d8a068fa38372308574">create_kernel</a>(kernel_name.str(), options));</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = (_bias == <span class="keyword">nullptr</span>) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor());</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="comment">// Calculate output right and bottom border</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_width = output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_height = output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_padding_right = <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(output_width, num_elems_written_per_iteration_x * _lws[0]) - output_width;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_padding_bottom = <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(output_height, num_elems_written_per_iteration_y * _lws[1]) - output_height;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// Calculate input right and bottom border</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_width = 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_height = 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_total_width = std::max(<span class="keywordtype">int</span>(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#a07b929c34ad1dc823d8315876aa403ce">padding</a>().<a class="code" href="structarm__compute_1_1_border_size.xhtml#a05374b750b0fc472c34ee61e6f028bba">left</a>), <span class="keywordtype">int</span>(_conv_pad_x)) + input_width + std::max(<span class="keywordtype">int</span>(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#a07b929c34ad1dc823d8315876aa403ce">padding</a>().<a class="code" href="structarm__compute_1_1_border_size.xhtml#a78b0fed184c642b78f32fd34b228a5f9">right</a>), <span class="keywordtype">int</span>(_conv_pad_x));</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_total_height = std::max(<span class="keywordtype">int</span>(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#a07b929c34ad1dc823d8315876aa403ce">padding</a>().<a class="code" href="structarm__compute_1_1_border_size.xhtml#a3fdd42ea34070a54e696b3adc28c4be3">top</a>), <span class="keywordtype">int</span>(_conv_pad_y)) + input_height + std::max(<span class="keywordtype">int</span>(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#a07b929c34ad1dc823d8315876aa403ce">padding</a>().<a class="code" href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">bottom</a>), <span class="keywordtype">int</span>(_conv_pad_y));</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_right1 = <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(input_total_width, num_elems_read_per_iteration_x * _lws[0]) - input_width - _conv_pad_x;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_bottom1 = <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(input_total_height, num_elems_read_per_iteration_y * _lws[1]) - input_height - _conv_pad_y;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> upper_bound_w = <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(((output_width + output_padding_right) * _conv_stride_x + (kernel_size - 1)), num_elems_read_per_iteration_x * _lws[0]) - _conv_pad_x - input_width;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> upper_bound_h = <a class="code" href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">ceil_to_multiple</a>(((output_height + output_padding_bottom) * _conv_stride_y + (kernel_size - 1)), num_elems_read_per_iteration_y * _lws[1]) - _conv_pad_y - input_height;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_right2 = std::max(upper_bound_w, _conv_pad_x);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_bottom2 = std::max(upper_bound_h, _conv_pad_y);</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_right = std::max(padding_right1, padding_right2);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> padding_bottom = std::max(padding_bottom1, padding_bottom2);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a> border = <a class="code" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a>(0, output_padding_right, output_padding_bottom, 0);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> win = <a class="code" href="namespacearm__compute.xhtml#affc1f59e0b2c29bf81e8c95bf0fa8e76">calculate_max_enlarged_window</a>(*output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="classarm__compute_1_1_steps.xhtml">Steps</a>(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, num_elems_written_per_iteration_z), border);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a> input_access(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a> weights_access = <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a>(<span class="keyword">nullptr</span>, 0, 0, 0, 0);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a> bias_access = <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a>(<span class="keyword">nullptr</span>, 0, 0, 0, 1);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">switch</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>())</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  {</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">if</span>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(2) % 2 != 0) || (kernel_size != 1))</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  {</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  weights_access = <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</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>(), 0, 0, kernel_size + 1, kernel_size);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  }</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keywordflow">if</span>(_bias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  {</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  bias_access = <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a>(_bias->info(), 0, 0, _bias->info()->dimension(0) + 1, 1);</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="keywordflow">break</span>;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  weights_access = <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</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>(), 0, 0, kernel_size, kernel_size);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">if</span>(_bias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  bias_access = <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a>(_bias->info(), 0, 0, _bias->info()->dimension(0), 1);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  }</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keywordflow">break</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="keywordflow">default</span>:</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Current data type is not supported"</span>);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">break</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> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <a class="code" href="classarm__compute_1_1_access_window_static.xhtml">AccessWindowStatic</a> output_access(output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);</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>  <span class="keywordflow">if</span>(_bias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  {</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <a class="code" href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">update_window_and_padding</a>(win, input_access, weights_access, bias_access, output_access);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  {</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">update_window_and_padding</a>(win, input_access, weights_access, output_access);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  }</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> </div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  output_access.set_valid_region(win, <a class="code" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()));</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> </div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  _kernel.set_argument(idx++, _weights->info()->strides_in_bytes()[3]); <span class="comment">// weights_stride_w</span></div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  _kernel.set_argument(idx++, _weights->info()->dimension(2)); <span class="comment">// weights_depth</span></div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  IGCKernel::configure(win);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> <span class="keyword">template</span> <<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_size></div><div class="line"><a name="l00401"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#a8fd12b95bdde3f93db96bc9b1598db69"> 401</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#a8fd12b95bdde3f93db96bc9b1598db69">GCDirectConvolutionLayerKernel<kernel_size>::run</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window)</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> {</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="_validate_8h.xhtml#a1b35b0d258183cf9ef36adf684d0b88c">ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL</a>(<span class="keyword">this</span>);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <a class="code" href="_validate_8h.xhtml#a6eb9ce82815fe429250189da7592ba75">ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">IKernel::window</a>(), window);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> </div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  _kernel.use();</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>  _output->set_needs_shifting(<span class="keyword">true</span>);</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="comment">// Get initial windows</span></div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a> = window.<a class="code" href="classarm__compute_1_1_window.xhtml#a30ca5bdbb60ee281d7f1ab34f7a4ee40">first_slice_window_3D</a>();</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> win_in = window;</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>  win_in.<a class="code" href="classarm__compute_1_1_window.xhtml#a69496c7cb46a4101813d7456a6bd097b">adjust</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, -_conv_pad_x, <span class="keyword">true</span>);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  win_in.<a class="code" href="classarm__compute_1_1_window.xhtml#a69496c7cb46a4101813d7456a6bd097b">adjust</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, -_conv_pad_y, <span class="keyword">true</span>);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  win_in.<a class="code" href="classarm__compute_1_1_window.xhtml#a55ed4ad2395fd25ba847cbf6c54b85e4">set_dimension_step</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, window.<a class="code" href="classarm__compute_1_1_window.xhtml#a273fd2ecdd45169b2f702f01a7e5e382">x</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a918580dc9188825d67dbb203a43d02fe">step</a>() * _conv_stride_x);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  win_in.<a class="code" href="classarm__compute_1_1_window.xhtml#a55ed4ad2395fd25ba847cbf6c54b85e4">set_dimension_step</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, window.<a class="code" href="classarm__compute_1_1_window.xhtml#a1b522b073f3ca32d24eb4e03495ef8a6">y</a>().<a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml#a918580dc9188825d67dbb203a43d02fe">step</a>() * _conv_stride_y);</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>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> slice_in = win_in.<a class="code" href="classarm__compute_1_1_window.xhtml#a30ca5bdbb60ee281d7f1ab34f7a4ee40">first_slice_window_3D</a>();</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx1 = 2 * num_arguments_per_3D_tensor();</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  add_3D_tensor_argument(idx1, _weights, 3, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</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="keywordflow">if</span>(_bias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  {</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> slice_bias;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  slice_bias.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(_bias->info()->tensor_shape());</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  add_1D_tensor_argument(idx1, _bias, 4, slice_bias);</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  }</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a>.shift(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, -(_output->info()->padding()).left);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">do</span></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="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  add_3D_tensor_argument(idx, _input, 1, slice_in);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  add_3D_tensor_argument(idx, _output, 2, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a>);</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>  _kernel.update_shader_params();</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <a class="code" href="namespacearm__compute.xhtml#a6e51ab3789678d3e0b0b72178dd6c4c6">enqueue</a>(*<span class="keyword">this</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a>, _lws);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  }</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">while</span>(window.<a class="code" href="classarm__compute_1_1_window.xhtml#aac792e3a11bc73bafafc4f4284c7f215">slide_window_slice_3D</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a>) && win_in.<a class="code" href="classarm__compute_1_1_window.xhtml#aac792e3a11bc73bafafc4f4284c7f215">slide_window_slice_3D</a>(slice_in));</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> }</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml">arm_compute::GCDirectConvolutionLayerKernel<1></a>;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml">arm_compute::GCDirectConvolutionLayerKernel<3></a>;</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml">arm_compute::GCDirectConvolutionLayerKernel<5></a>;</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="structarm__compute_1_1_border_size_xhtml_a3fdd42ea34070a54e696b3adc28c4be3"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a3fdd42ea34070a54e696b3adc28c4be3">arm_compute::BorderSize::top</a></div><div class="ttdeci">unsigned int top</div><div class="ttdoc">top of the border</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00339">Types.h:339</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_kernel_xhtml_ad34a46f53686c12a5c5e717cc9617fb6"><div class="ttname"><a href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">arm_compute::IKernel::window</a></div><div class="ttdeci">const Window & window() const</div><div class="ttdoc">The maximum window the kernel can be executed on.</div><div class="ttdef"><b>Definition:</b> <a href="_i_kernel_8cpp_source.xhtml#l00028">IKernel.cpp:28</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="_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#l00035">CLTensor.cpp:35</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="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">ConvolutionLayer.cpp:175</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_abbe3399a66c35c3c353030cd0a84c936"><div class="ttname"><a href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00387">Utils.cpp:387</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a5befbfaf6bc224eabc58b5e88b1de6d1"><div class="ttname"><a href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00543">Validate.h:543</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml">arm_compute::BorderSize</a></div><div class="ttdoc">Container for 2D border size.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00259">Types.h:259</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a6e51ab3789678d3e0b0b72178dd6c4c6"><div class="ttname"><a href="namespacearm__compute.xhtml#a6e51ab3789678d3e0b0b72178dd6c4c6">arm_compute::enqueue</a></div><div class="ttdeci">void enqueue(cl::CommandQueue &queue, ICLKernel &kernel, const Window &window, const cl::NDRange &lws_hint=CLKernelLibrary::get().default_ndrange(), bool use_dummy_work_items=false)</div><div class="ttdoc">Add the kernel to the command queue with the given window.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_kernel_8cpp_source.xhtml#l00039">ICLKernel.cpp:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml_a918580dc9188825d67dbb203a43d02fe"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml#a918580dc9188825d67dbb203a43d02fe">arm_compute::Window::Dimension::step</a></div><div class="ttdeci">constexpr int step() const</div><div class="ttdoc">Return the step of the dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00102">Window.h:102</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#l00599">Winograd.cpp:599</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1support_1_1cpp11_xhtml_acc5dddee1cbe93a4eaf0a9f74ee96bb7"><div class="ttname"><a href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">arm_compute::support::cpp11::to_string</a></div><div class="ttdeci">std::string to_string(T &&value)</div><div class="ttdoc">Convert integer and float values to string.</div><div class="ttdef"><b>Definition:</b> <a href="_toolchain_support_8h_source.xhtml#l00242">ToolchainSupport.h:242</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00223">TensorInfo.h:223</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</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="_i_g_c_tensor_8h_xhtml"><div class="ttname"><a href="_i_g_c_tensor_8h.xhtml">IGCTensor.h</a></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#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a635f1895d94050329b7da12850d1a056"><div class="ttname"><a href="namespacearm__compute.xhtml#a635f1895d94050329b7da12850d1a056">arm_compute::string_from_activation_func</a></div><div class="ttdeci">const std::string & string_from_activation_func(ActivationLayerInfo::ActivationFunction act)</div><div class="ttdoc">Translates a given activation function to a string.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00170">Utils.cpp:170</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_g_c_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_g_c_tensor.xhtml">arm_compute::IGCTensor</a></div><div class="ttdoc">Interface for GLES Compute tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_g_c_tensor_8h_source.xhtml#l00035">IGCTensor.h:35</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a802ffcf1b49237efe5be8a314d3f3869"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">arm_compute::BorderSize::bottom</a></div><div class="ttdeci">unsigned int bottom</div><div class="ttdoc">bottom of the border</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00341">Types.h:341</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a0a7bb17a0a0414a7162f635776a02eb5"><div class="ttname"><a href="namespacearm__compute.xhtml#a0a7bb17a0a0414a7162f635776a02eb5">arm_compute::lower_string</a></div><div class="ttdeci">std::string lower_string(const std::string &val)</div><div class="ttdoc">Lower a given string.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00327">Utils.cpp:327</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#l01517">Types.h:1517</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a14470b4cb59140a1b6ff3b8f16c89ab6"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">arm_compute::Window::use_tensor_dimensions</a></div><div class="ttdeci">void use_tensor_dimensions(const TensorShape &shape, size_t first_dimension=Window::DimX)</div><div class="ttdoc">Use the tensor's dimensions to fill the window dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00250">Window.inl:250</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 &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#l00201">Helpers.inl:201</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_access_window_static_xhtml"><div class="ttname"><a href="classarm__compute_1_1_access_window_static.xhtml">arm_compute::AccessWindowStatic</a></div><div class="ttdoc">Implementation of a static rectangular access pattern.</div><div class="ttdef"><b>Definition:</b> <a href="_access_window_static_8h_source.xhtml#l00046">AccessWindowStatic.h:46</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#l00256">TensorInfo.h:256</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_direct_convolution_layer_kernel_xhtml_ab904c60f24f8f8f44e24d71f337c86a9"><div class="ttname"><a href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#ab904c60f24f8f8f44e24d71f337c86a9">arm_compute::GCDirectConvolutionLayerKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *bias, IGCTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info=ActivationLayerInfo())</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_direct_convolution_layer_kernel_8cpp_source.xhtml#l00053">GCDirectConvolutionLayerKernel.cpp:53</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_affc1f59e0b2c29bf81e8c95bf0fa8e76"><div class="ttname"><a href="namespacearm__compute.xhtml#affc1f59e0b2c29bf81e8c95bf0fa8e76">arm_compute::calculate_max_enlarged_window</a></div><div class="ttdeci">Window calculate_max_enlarged_window(const ValidRegion &valid_region, const Steps &steps=Steps(), BorderSize border_size=BorderSize())</div><div class="ttdoc">Calculate the maximum window for a given tensor shape and border setting.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_helpers_8cpp_source.xhtml#l00082">Helpers.cpp:82</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a13622133d9b41900a6a3e8f89e59a78b"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b">arm_compute::TensorInfo::num_dimensions</a></div><div class="ttdeci">size_t num_dimensions() const override</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00244">TensorInfo.h:244</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_direct_convolution_layer_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml">arm_compute::GCDirectConvolutionLayerKernel</a></div><div class="ttdoc">Interface for the direct convolution kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_direct_convolution_layer_kernel_8h_source.xhtml#l00037">GCDirectConvolutionLayerKernel.h:37</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="namespacearm__compute_xhtml_afc4bd8e872567d9c4c57d89eb0bb3da1"><div class="ttname"><a href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">arm_compute::update_window_and_padding</a></div><div class="ttdeci">bool update_window_and_padding(Window &win, Ts &&... patterns)</div><div class="ttdoc">Update window and padding size for each of the access patterns.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00402">Helpers.h:402</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a9cd394c15b73f79ca1d98f5328064be2"><div class="ttname"><a href="namespacearm__compute.xhtml#a9cd394c15b73f79ca1d98f5328064be2">arm_compute::float_to_string_with_full_precision</a></div><div class="ttdeci">std::string float_to_string_with_full_precision(float val)</div><div class="ttdoc">Create a string with the float in full precision.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01066">Utils.h:1066</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_direct_convolution_layer_kernel_xhtml_a8fd12b95bdde3f93db96bc9b1598db69"><div class="ttname"><a href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#a8fd12b95bdde3f93db96bc9b1598db69">arm_compute::GCDirectConvolutionLayerKernel::run</a></div><div class="ttdeci">void run(const Window &window) override</div><div class="ttdoc">Enqueue the OpenGL ES shader to process the given window.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_direct_convolution_layer_kernel_8cpp_source.xhtml#l00401">GCDirectConvolutionLayerKernel.cpp:401</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab237a0a375cf382d52b61653248d3d4a"><div class="ttname"><a href="namespacearm__compute.xhtml#ab237a0a375cf382d52b61653248d3d4a">arm_compute::ceil_to_multiple</a></div><div class="ttdeci">auto ceil_to_multiple(S value, T divisor) -> decltype(((value+divisor - 1)/divisor) *divisor)</div><div class="ttdoc">Computes the smallest number larger or equal to value that is a multiple of divisor.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l00066">Utils.h:66</a></div></div> |
| <div class="ttc" id="_g_c_kernel_library_8h_xhtml"><div class="ttname"><a href="_g_c_kernel_library_8h.xhtml">GCKernelLibrary.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_steps_xhtml"><div class="ttname"><a href="classarm__compute_1_1_steps.xhtml">arm_compute::Steps</a></div><div class="ttdoc">Class to describe a number of elements in each dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_steps_8h_source.xhtml#l00040">Steps.h:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_direct_convolution_layer_kernel_xhtml_a423f9a45a52983b4de5e2b347f4369c7"><div class="ttname"><a href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#a423f9a45a52983b4de5e2b347f4369c7">arm_compute::GCDirectConvolutionLayerKernel::border_size</a></div><div class="ttdeci">BorderSize border_size() const override</div><div class="ttdoc">The size of the border for that kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_direct_convolution_layer_kernel_8cpp_source.xhtml#l00047">GCDirectConvolutionLayerKernel.cpp:47</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</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_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item.</div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</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="_validate_8h_xhtml_ad9fd47433ba6091668c207e21dd6385f"><div class="ttname"><a href="_validate_8h.xhtml#ad9fd47433ba6091668c207e21dd6385f">ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00286">Validate.h:286</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#l00676">Types.h:676</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a07b929c34ad1dc823d8315876aa403ce"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a07b929c34ad1dc823d8315876aa403ce">arm_compute::ITensorInfo::padding</a></div><div class="ttdeci">virtual PaddingSize padding() const =0</div><div class="ttdoc">Padding of tensor.</div></div> |
| <div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a3a77be8aebd8e00522b32061d46ccdbd"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">arm_compute::test::validation::bias</a></div><div class="ttdeci">CLTensor bias</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00181">ConvolutionLayer.cpp:181</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_abc72c95941485d8a068fa38372308574"><div class="ttname"><a href="namespacearm__compute.xhtml#abc72c95941485d8a068fa38372308574">arm_compute::create_kernel</a></div><div class="ttdeci">std::unique_ptr< Kernel > create_kernel()</div><div class="ttdoc">Helper function to create and return a unique_ptr pointed to a CL/GLES kernel object.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00086">Helpers.h:86</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a05374b750b0fc472c34ee61e6f028bba"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a05374b750b0fc472c34ee61e6f028bba">arm_compute::BorderSize::left</a></div><div class="ttdeci">unsigned int left</div><div class="ttdoc">left of the border</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00342">Types.h:342</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC</a></div><div class="ttdoc">Logistic ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aac792e3a11bc73bafafc4f4284c7f215"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aac792e3a11bc73bafafc4f4284c7f215">arm_compute::Window::slide_window_slice_3D</a></div><div class="ttdeci">bool slide_window_slice_3D(Window &slice) const</div><div class="ttdoc">Slide the passed 3D window slice.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00319">Window.h:319</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a78b0fed184c642b78f32fd34b228a5f9"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a78b0fed184c642b78f32fd34b228a5f9">arm_compute::BorderSize::right</a></div><div class="ttdeci">unsigned int right</div><div class="ttdoc">right of the border</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00340">Types.h:340</a></div></div> |
| <div class="ttc" id="_g_c_helpers_8h_xhtml"><div class="ttname"><a href="_g_c_helpers_8h.xhtml">GCHelpers.h</a></div></div> |
| <div class="ttc" id="_g_c_direct_convolution_layer_kernel_8h_xhtml"><div class="ttname"><a href="_g_c_direct_convolution_layer_kernel_8h.xhtml">GCDirectConvolutionLayerKernel.h</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00789">Validate.h:789</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_kernel_library_xhtml_a677e1b4dae70aaa7a18e9a8f1a4692e8"><div class="ttname"><a href="classarm__compute_1_1_g_c_kernel_library.xhtml#a677e1b4dae70aaa7a18e9a8f1a4692e8">arm_compute::GCKernelLibrary::get</a></div><div class="ttdeci">static GCKernelLibrary & get()</div><div class="ttdoc">Get the static instance of GCKernelLibrary.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_kernel_library_8cpp_source.xhtml#l00334">GCKernelLibrary.cpp:334</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a55ed4ad2395fd25ba847cbf6c54b85e4"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a55ed4ad2395fd25ba847cbf6c54b85e4">arm_compute::Window::set_dimension_step</a></div><div class="ttdeci">void set_dimension_step(size_t dimension, int step)</div><div class="ttdoc">Set the step of a given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00153">Window.inl:153</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a1b522b073f3ca32d24eb4e03495ef8a6"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a1b522b073f3ca32d24eb4e03495ef8a6">arm_compute::Window::y</a></div><div class="ttdeci">constexpr const Dimension & y() const</div><div class="ttdoc">Alias to access the second dimension of the window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00152">Window.h:152</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="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="_access_window_static_8h_xhtml"><div class="ttname"><a href="_access_window_static_8h.xhtml">AccessWindowStatic.h</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_valid_region_xhtml"><div class="ttname"><a href="structarm__compute_1_1_valid_region.xhtml">arm_compute::ValidRegion</a></div><div class="ttdoc">Container for valid region of a window.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00174">Types.h:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_c_direct_convolution_layer_kernel_xhtml_ab55e3264789da35ae1297fefa4efed7c"><div class="ttname"><a href="classarm__compute_1_1_g_c_direct_convolution_layer_kernel.xhtml#ab55e3264789da35ae1297fefa4efed7c">arm_compute::GCDirectConvolutionLayerKernel::GCDirectConvolutionLayerKernel</a></div><div class="ttdeci">GCDirectConvolutionLayerKernel()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_direct_convolution_layer_kernel_8cpp_source.xhtml#l00041">GCDirectConvolutionLayerKernel.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a69496c7cb46a4101813d7456a6bd097b"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a69496c7cb46a4101813d7456a6bd097b">arm_compute::Window::adjust</a></div><div class="ttdeci">void adjust(size_t dimension, int adjust_value, bool is_at_start)</div><div class="ttdoc">Adjust the start or end of a given dimension by the given value.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00126">Window.inl:126</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a30ca5bdbb60ee281d7f1ab34f7a4ee40"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a30ca5bdbb60ee281d7f1ab34f7a4ee40">arm_compute::Window::first_slice_window_3D</a></div><div class="ttdeci">Window first_slice_window_3D() const</div><div class="ttdoc">First 3D slice of the window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00275">Window.h:275</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="_validate_8h_xhtml_a6eb9ce82815fe429250189da7592ba75"><div class="ttname"><a href="_validate_8h.xhtml#a6eb9ce82815fe429250189da7592ba75">ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00205">Validate.h:205</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> |
| <div class="ttc" id="_validate_8h_xhtml_a1b35b0d258183cf9ef36adf684d0b88c"><div class="ttname"><a href="_validate_8h.xhtml#a1b35b0d258183cf9ef36adf684d0b88c">ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00940">Validate.h:940</a></div></div> |
| <div class="ttc" id="_i_access_window_8h_xhtml"><div class="ttname"><a href="_i_access_window_8h.xhtml">IAccessWindow.h</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a548131b3d37da47a2e9d32111c88dfe1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">arm_compute::test::validation::reference::slice</a></div><div class="ttdeci">SimpleTensor< T > slice(const SimpleTensor< T > &src, Coordinates starts, Coordinates ends)</div><div class="ttdef"><b>Definition:</b> <a href="_slice_operations_8cpp_source.xhtml#l00038">SliceOperations.cpp:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a273fd2ecdd45169b2f702f01a7e5e382"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a273fd2ecdd45169b2f702f01a7e5e382">arm_compute::Window::x</a></div><div class="ttdeci">constexpr const Dimension & x() const</div><div class="ttdoc">Alias to access the first dimension of the window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00143">Window.h:143</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div> |
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