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| <a href="_n_e_winograd_convolution_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_n_e_winograd_convolution_layer_8h.xhtml">arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.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="_error_8h.xhtml">arm_compute/core/Error.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_n_e_winograd_convolution_layer_kernel_8h.xhtml">arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</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="l00030"></a><span class="lineno"> 30</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="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_shape_calculator_8h.xhtml">arm_compute/core/utils/misc/ShapeCalculator.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_n_e_scheduler_8h.xhtml">arm_compute/runtime/NEON/NEScheduler.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_n_e_g_e_m_m_assembly_dispatch_8h.xhtml">arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "arm_compute/core/NEON/kernels/convolution/common/utils.hpp"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd.hpp"</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">inline</span> Status validate_kernel_3x3(<span class="keyword">const</span> Size2D input_dims, <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</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="keywordflow">if</span>(input_dims.width > 4 && input_dims.height > 4)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  }</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</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="l00060"></a><span class="lineno"> 60</span>  {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="keyword">inline</span> Status validate_kernel_5x5(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</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="l00073"></a><span class="lineno"> 73</span>  {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  }</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="keyword">inline</span> Status validate_kernel_3x1(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</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="l00086"></a><span class="lineno"> 86</span>  {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="keyword">inline</span> Status validate_kernel_1x3(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</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="l00100"></a><span class="lineno"> 100</span>  {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</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>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="keyword">inline</span> Status validate_kernel_5x1(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</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="l00113"></a><span class="lineno"> 113</span>  {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="keyword">inline</span> Status validate_kernel_1x5(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</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="l00125"></a><span class="lineno"> 125</span>  {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</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">return</span> Status{};</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> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="keyword">inline</span> Status validate_kernel_7x1(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</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="l00138"></a><span class="lineno"> 138</span>  {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> }</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="keyword">inline</span> Status validate_kernel_1x7(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> TensorInfo *input0, <span class="keyword">const</span> TensorInfo *input1, <span class="keyword">const</span> TensorInfo *batched_mm_output,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> WinogradInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>((<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>::validate</a>(batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)));</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled())</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">NEActivationLayer::validate</a>(output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  }</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> }</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="keyword">inline</span> Tensor4DShape internal_get_input_shape(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout();</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_width = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>));</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_channels = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>));</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_batches = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(3);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">return</span> Tensor4DShape{ in_batches, in_height, in_width, in_channels };</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> Status validate_arguments(<span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> ITensorInfo *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> ITensorInfo *biases, <span class="keyword">const</span> ITensorInfo *output, <span class="keyword">const</span> PadStrideInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(output);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first != 1 || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().second != 1, <span class="stringliteral">"Winograd layer only supports unit strides."</span>);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  {</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, biases);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases->num_dimensions() > 1);</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">return</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml#a8b4165c2e7c5c983b930a0f5f4df6acf">INEWinogradLayerTransformWeightsKernel<float>::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> Size2D winograd_output_tile(<span class="keyword">const</span> Size2D &input_dims, <span class="keyword">const</span> Size2D &kernel_dims)</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>  Size2D output_tile = Size2D{};</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">if</span>(kernel_dims == Size2D(3U, 3U))</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>  output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);</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">else</span> <span class="keywordflow">if</span>(kernel_dims == Size2D(5U, 5U))</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  output_tile = Size2D(2U, 2U);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_dims == Size2D(1U, 3U))</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>  output_tile = Size2D(1U, 6U);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_dims == Size2D(3U, 1U))</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>  output_tile = Size2D(6U, 1U);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  }</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_dims == Size2D(1U, 5U))</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  output_tile = Size2D(1U, 4U);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_dims == Size2D(5U, 1U))</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  output_tile = Size2D(4U, 1U);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  }</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_dims == Size2D(7U, 1U))</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  output_tile = Size2D(2U, 1U);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  }</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_dims == Size2D(1U, 7U))</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  output_tile = Size2D(1U, 2U);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">return</span> output_tile;</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> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="keywordtype">bool</span> check_support_fast_math(<span class="keyword">const</span> Size2D &output_tile, <span class="keyword">const</span> Size2D &kernel_size)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">// Check if we want to configure a Winograd configuration which requires fast math</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">using</span> WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;</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="keyword">const</span> std::vector<WinogradConfiguration> fast_math_winograd =</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>  WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</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="keyword">auto</span> p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  std::pair<int, int>(kernel_size.width, kernel_size.height));</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordflow">return</span> std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end();</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> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> fuse_function_supported(<span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">return</span> <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> ||</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <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#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_gemm::Activation</a> arm_gemm_activation_from_acl_activation(<span class="keyword">const</span> ActivationLayerInfo &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">switch</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation())</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  {</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">case</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>:</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  {</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_gemm::Activation</a>(arm_gemm::Activation::Type::ReLU, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.a(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.b());</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">case</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>:</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  {</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_gemm::Activation</a>(arm_gemm::Activation::Type::BoundedReLU, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.a(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.b());</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  }</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_gemm::Activation</a>(arm_gemm::Activation::Type::None);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> }</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> } <span class="comment">//namespace</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a83d886e7456d6a5d67ca145efd4c1aff"> 263</a></span> <a class="code" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a83d886e7456d6a5d67ca145efd4c1aff">NEWinogradConvolutionLayer::NEWinogradConvolutionLayer</a>(<span class="keyword">const</span> std::shared_ptr<IMemoryManager> &memory_manager)</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  : _memory_group(memory_manager), _gemm_function(memory_manager), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), _activationlayer_function(),</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  _permute_input(), _permute_weights(), _permute_output(), _input_transformed(), _output_transformed(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(),</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  _weights_hwio(), _input(), _weights(), _output(), _is_prepared(false), _is_activationlayer_enabled(false)</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> }</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a49b542b1a17cd73034736acfa562a8ec"> 270</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a49b542b1a17cd73034736acfa562a8ec">NEWinogradConvolutionLayer::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, <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="l00271"></a><span class="lineno"> 271</span>  <span class="keywordtype">bool</span> enable_fast_math)</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> {</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(validate_arguments(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), (biases != <span class="keyword">nullptr</span>) ? biases-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>, 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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// Get indices for the width and height</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout();</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dims = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(width_idx), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(height_idx));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</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>(width_idx), <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>(height_idx));</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile = winograd_output_tile(input_dims, kernel_size);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</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> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">if</span>(!enable_fast_math)</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>(check_support_fast_math(output_tile, kernel_size), <span class="stringliteral">"This Winograd configuration requires enable_fast_math=true"</span>);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  }</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  _weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  _input = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  _output = output;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordtype">int</span> n_gemms = 0;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordtype">int</span> N_BLOCK = 0; <span class="comment">// Size of block used by GEMM.</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(3, 3))</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  {</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(width_idx) > 4 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(height_idx) > 4)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3></a>;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3></a>;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  }</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  }</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(5, 5))</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5></a>;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  }</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1, 3))</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  {</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1></a>;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(3, 1))</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>  <span class="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3></a>;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1, 5))</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  {</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1></a>;</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  }</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(5, 1))</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  {</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5></a>;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1, 7))</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="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1></a>;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(7, 1))</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="keyword">using</span> config = <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7></a>;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  n_gemms = config::WinogradBase::N_GEMMS;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  N_BLOCK = config::WinogradConv::N_BLOCK;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  }</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">else</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>  <a class="code" href="_error_8h.xhtml#a7cf8d8b669b8f7b05680230be30d60f4">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Not supported."</span>);</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> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keyword">const</span> PaddingType use_padding_type = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> use_same_padding = use_padding_type == PADDING_SAME;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_channels = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(channel_idx);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_channels = 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>(channel_idx);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> Tensor4DShape in_shape(internal_get_input_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>));</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type();</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> data_type_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->element_size();</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="comment">// Get the memory required to instantiate a new Winograd operator.</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  constexpr <span class="keywordtype">size_t</span> storage_alignment = 64;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="comment">// Kernel Storage</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  in_channels)</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  * data_type_size;</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="comment">// Input storage</span></div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  use_same_padding)</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  * data_type_size;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="comment">// Output storage</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a> = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_matrix_stride = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="comment">// Configure GEMM</span></div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> tile_rows = <a class="code" href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.first, output_tile.height);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> tile_cols = <a class="code" href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.second, output_tile.width);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> m = in_shape.n_batches * tile_rows * tile_cols;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> k = in_shape.n_channels;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> n = out_channels;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> kernel_matrix_row_stride = <a class="code" href="utils_8hpp.xhtml#a8f6fbf8b243a10af40ce8d47a1013384">roundup</a>(out_channels, N_BLOCK);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> output_matrix_row_stride = kernel_matrix_row_stride;</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> a_shape(k, m, 1, n_gemms);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">Strides</a> a_strides(data_type_size);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  a_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(1, a_strides[0] * k);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="comment">//a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  a_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(2, 0);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  a_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(3, data_type_size * input_matrix_stride);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> b_shape(n, k, n_gemms);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">Strides</a> b_strides(data_type_size);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  b_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(1, data_type_size * kernel_matrix_row_stride);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  b_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(2, data_type_size * kernel_matrix_stride);</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>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> d_shape(n, m, 1, n_gemms);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">Strides</a> d_strides(data_type_size);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  d_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(1, data_type_size * output_matrix_row_stride);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">//d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  d_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(2, 0);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  d_strides.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">set</a>(3, data_type_size * output_matrix_stride);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> a_info{};</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> b_info{};</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> d_info{};</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  a_info.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(a_shape, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, a_strides, 0, input_storage_size);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  b_info.init(b_shape, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, b_strides, 0, kernel_storage_size);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  d_info.init(d_shape, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>, d_strides, 0, output_storage_size);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  _input_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(a_info, storage_alignment);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  _kernel_storage.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(b_info, storage_alignment);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  _output_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(d_info, storage_alignment);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> </div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="comment">// configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2), _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="l00466"></a><span class="lineno"> 466</span>  _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1), _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>(3)),</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  1, _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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  _output_nhwc.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_to_use = _input;</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output_to_use = _output;</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">PermutationVector</a> weights_permutation_vector(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_num_threads = <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">num_threads</a>();</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="comment">// Configure the kernel to transform the input tensor from NCHW -> NHWC</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  {</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_input_nhwc);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  _permute_input.<a class="code" href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &_input_nhwc, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  input_to_use = &_input_nhwc;</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  weights_permutation_vector = <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  }</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="comment">// Configure input transform kernel</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_input_transformed);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_input_workspace);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  &_input_transformed, input_matrix_stride, &_input_workspace);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_workspace_info(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_workspace_size), 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="l00491"></a><span class="lineno"> 491</span>  _input_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(input_workspace_info);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  _input_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  {</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  _input_nhwc.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  }</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span> </div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="comment">// Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  _permute_weights.<a class="code" href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_weights_hwio, weights_permutation_vector);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="comment">// Configure GEMM function</span></div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_output_transformed);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  _gemm_function.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">configure</a>(&_input_transformed, &_kernel_storage, <span class="keyword">nullptr</span>, &_output_transformed, 1.0f, 0.f);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  _input_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="comment">// Configure output transform function</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="comment">// The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  {</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_output_nhwc);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  output_to_use = &_output_nhwc;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  }</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_gemm::Activation</a> activation = arm_gemm_activation_from_acl_activation(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  transform_output_kernel->configure(biases,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  &_output_transformed,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  output_matrix_stride,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  output_to_use,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  in_shape.n_batches,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.first,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">output_shape</a>.second,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  out_channels,</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  &_output_workspace,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  activation);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_workspace_info(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(output_workspace_size), 1, _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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  _output_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(output_workspace_info);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  _output_workspace.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  _output_transformed.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="comment">// Reorder the convoluted output to ACL's ordering NCHW</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  {</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  _permute_output.<a class="code" href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">configure</a>(&_output_nhwc, _output, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  _output_nhwc.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  }</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  _transform_input_kernel = std::move(transform_input_kernel);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  _transform_weights_kernel = std::move(transform_weights_kernel);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  _transform_output_kernel = std::move(transform_output_kernel);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">//Configure Activation Layer</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  _is_activationlayer_enabled = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled() && ! fuse_function_supported(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  {</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">configure</a>(_output, <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  }</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> }</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> </div><div class="line"><a name="l00552"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 552</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">NEWinogradConvolutionLayer::run</a>()</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> {</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = _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#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  {</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="comment">//Bring channels to the front as Winograd code expects the tensor to be in the format NHWC</span></div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  _permute_input.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  }</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="comment">// Transform input tensor to the winograd domain</span></div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_transform_input_kernel.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> </div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="comment">//Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs</span></div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  _gemm_function.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="comment">// Transform output tensor to the spatial domain</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_transform_output_kernel.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  {</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="comment">// Reorder the convoluted output to ACL's ordering NCHW</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  _permute_output.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  }</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled )</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  {</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  }</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> }</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> </div><div class="line"><a name="l00587"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a1c5a3dc6ea10d1f68d76064b82b8b5c2"> 587</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a1c5a3dc6ea10d1f68d76064b82b8b5c2">NEWinogradConvolutionLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <span class="keywordtype">bool</span> enable_fast_math)</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> {</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_arguments(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>));</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="comment">// Get indices for the width and height</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> </div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dims = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_width), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_height));</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_width), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_height));</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile = winograd_output_tile(input_dims, kernel_size);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="comment">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="keywordflow">if</span>(!enable_fast_math)</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  {</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(check_support_fast_math(output_tile, kernel_size), <span class="stringliteral">"This Winograd configuration requires enable_fast_math=true"</span>);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  }</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span> </div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a> = <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a>(output_tile,</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  kernel_size,</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  input_dims,</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout());</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="comment">// Validate input transform</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input0_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">misc::shape_calculator::compute_winograd_input_transform_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input0 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->clone()->set_tensor_shape(input0_shape);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="comment">// Validate filter transform</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input1_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">misc::shape_calculator::compute_winograd_filter_transform_shape</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#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input1 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->set_tensor_shape(input1_shape);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="comment">// Validate batched matrix multiply</span></div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> batched_mm_output_shape = input0.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>();</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  batched_mm_output_shape[0] = input1.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[0];</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> batched_mm_output = input0.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">clone</a>()->set_tensor_shape(batched_mm_output_shape);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(3, 3))</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  {</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordflow">return</span> validate_kernel_3x3(input_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  }</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(5, 5))</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  {</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left(), <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keywordflow">return</span> validate_kernel_5x5(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  }</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(3, 1))</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="keywordflow">return</span> validate_kernel_3x1(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  }</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1, 3))</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  {</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 1, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">return</span> validate_kernel_1x3(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  }</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(5, 1))</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  {</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keywordflow">return</span> validate_kernel_5x1(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  }</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1, 5))</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  {</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 2, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordflow">return</span> validate_kernel_1x5(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  }</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(7, 1))</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  {</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keywordflow">return</span> validate_kernel_7x1(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  }</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_size == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1, 7))</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  {</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() != 3, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() != 0u && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() != 0, <span class="stringliteral">"Only SAME or VALID padding supported"</span>);</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keywordflow">return</span> validate_kernel_1x7(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &input0, &input1, &batched_mm_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  }</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  {</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="_error_8h.xhtml#a9561091f3566e78ad3aa39259bc4126a">ARM_COMPUTE_RETURN_ERROR_MSG</a>(<span class="stringliteral">"Kernel shape not supported"</span>);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  }</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> }</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div><div class="line"><a name="l00695"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 695</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">NEWinogradConvolutionLayer::prepare</a>()</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  {</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="comment">// Permute weights</span></div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  _weights_hwio.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  _permute_weights.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  _weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="comment">// Transform weights</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  _kernel_storage.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_transform_weights_kernel.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>);</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span> </div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  _weights_hwio.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  }</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> }</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> </div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="utils_8hpp_xhtml_aa4508679e1d089c2bdcf000f72357683"><div class="ttname"><a href="utils_8hpp.xhtml#aa4508679e1d089c2bdcf000f72357683">iceildiv</a></div><div class="ttdeci">T iceildiv(const T a, const T b)</div><div class="ttdef"><b>Definition:</b> <a href="utils_8hpp_source.xhtml#l00038">utils.hpp:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a04249f91ec2964d21a91bb7038821000"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd input transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00667">ShapeCalculator.h:667</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_afbc359bde9be72a6edab175978e56662"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">arm_compute::TensorInfo::clone</a></div><div class="ttdeci">std::unique_ptr< ITensorInfo > clone() const override</div><div class="ttdoc">Provide a clone of the current object of class T.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00314">TensorInfo.cpp:314</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_convolution_layer_xhtml_a83d886e7456d6a5d67ca145efd4c1aff"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a83d886e7456d6a5d67ca145efd4c1aff">arm_compute::NEWinogradConvolutionLayer::NEWinogradConvolutionLayer</a></div><div class="ttdeci">NEWinogradConvolutionLayer(const std::shared_ptr< IMemoryManager > &memory_manager=nullptr)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00263">NEWinogradConvolutionLayer.cpp:263</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">ConvolutionLayer.cpp:183</a></div></div> |
| <div class="ttc" id="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="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel_xhtml_a6d3d1d4d66f89908455bc5f05e259ce9"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">arm_compute::NEWinogradLayerTransformInputKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEWinogradLayerTransform...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_kernel_8cpp_source.xhtml#l00380">NEWinogradConvolutionLayerKernel.cpp:380</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="_error_8h_xhtml_a7cf8d8b669b8f7b05680230be30d60f4"><div class="ttname"><a href="_error_8h.xhtml#a7cf8d8b669b8f7b05680230be30d60f4">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(msg)</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#l00352">Error.h:352</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml">arm_compute::WinogradInfo</a></div><div class="ttdoc">Winograd information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l02154">Types.h:2154</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00597">Winograd.cpp:597</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00204">Error.h:204</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_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="classarm__compute_1_1_n_e_activation_layer_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::NEActivationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)</div><div class="ttdoc">[NEActivationLayer snippet]</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_8cpp_source.xhtml#l00043">NEActivationLayer.cpp:43</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00048">Types.h:48</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00328">Winograd.cpp:328</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div> |
| <div class="ttc" id="_n_e_g_e_m_m_assembly_dispatch_8h_xhtml"><div class="ttname"><a href="_n_e_g_e_m_m_assembly_dispatch_8h.xhtml">NEGEMMAssemblyDispatch.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a5f9016ea3e28a033b7cc216bdda912be"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a5f9016ea3e28a033b7cc216bdda912be">arm_compute::graph::Activation</a></div><div class="ttdeci">arm_compute::ActivationLayerInfo::ActivationFunction Activation</div><div class="ttdoc">Constant TensorID specifying an equivalent of null tensor.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00068">Types.h:68</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_convolution_layer_xhtml_a49b542b1a17cd73034736acfa562a8ec"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a49b542b1a17cd73034736acfa562a8ec">arm_compute::NEWinogradConvolutionLayer::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info=ActivationLayerInfo(), bool enable_fast_math=false)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00270">NEWinogradConvolutionLayer.cpp:270</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a982730e6f0da5f9490f59bc5f6bb3f27"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">arm_compute::Dimensions::set</a></div><div class="ttdeci">void set(size_t dimension, T value)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00074">Dimensions.h:74</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00296">Error.h:296</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01615">Types.h:1615</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2020 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel_xhtml_a6d3d1d4d66f89908455bc5f05e259ce9"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">arm_compute::NEWinogradLayerTransformWeightsKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEWinogradLayerTransform...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_kernel_8cpp_source.xhtml#l00247">NEWinogradConvolutionLayerKernel.cpp:247</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel_xhtml_a8b4165c2e7c5c983b930a0f5f4df6acf"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml#a8b4165c2e7c5c983b930a0f5f4df6acf">arm_compute::INEWinogradLayerTransformWeightsKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEWinogradLayerTransform...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_kernel_8cpp_source.xhtml#l00166">NEWinogradConvolutionLayerKernel.cpp:166</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_convolution_layer_xhtml_a1c5a3dc6ea10d1f68d76064b82b8b5c2"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#a1c5a3dc6ea10d1f68d76064b82b8b5c2">arm_compute::NEWinogradConvolutionLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info=ActivationLayerInfo(), bool enable_fast_math=false)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMConvolutionLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00587">NEWinogradConvolutionLayer.cpp:587</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMM::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM.cpp:285</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00152">Error.h:152</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel.</div></div> |
| <div class="ttc" id="_n_e_winograd_convolution_layer_8h_xhtml"><div class="ttname"><a href="_n_e_winograd_convolution_layer_8h.xhtml">NEWinogradConvolutionLayer.h</a></div></div> |
| <div class="ttc" id="_n_e_winograd_convolution_layer_kernel_8h_xhtml"><div class="ttname"><a href="_n_e_winograd_convolution_layer_kernel_8h.xhtml">NEWinogradConvolutionLayerKernel.h</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a0b0eb3235749a2909dc5a101afe59a1b"><div class="ttname"><a href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00456">Error.h:456</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00686">Types.h:686</a></div></div> |
| <div class="ttc" id="_n_e_scheduler_8h_xhtml"><div class="ttname"><a href="_n_e_scheduler_8h.xhtml">NEScheduler.h</a></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_1misc_1_1shape__calculator_xhtml_a25e3751f07d4b2771a05d8d01a7f7620"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd filter transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00644">ShapeCalculator.h:644</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::TensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00148">TensorAllocator.cpp:148</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEWinogradConvolutionLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00695">NEWinogradConvolutionLayer.cpp:695</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ad6b64f33be1e66dcf7612483ffb8fd63"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">arm_compute::TensorInfo::init</a></div><div class="ttdeci">void init(Format format)</div><div class="ttdoc">Initialize the tensor info with just a format.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00107">TensorInfo.cpp:107</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_strides_xhtml"><div class="ttname"><a href="classarm__compute_1_1_strides.xhtml">arm_compute::Strides</a></div><div class="ttdoc">Strides of an item in bytes.</div><div class="ttdef"><b>Definition:</b> <a href="_strides_8h_source.xhtml#l00037">Strides.h:37</a></div></div> |
| <div class="ttc" id="utils_8hpp_xhtml_a8f6fbf8b243a10af40ce8d47a1013384"><div class="ttname"><a href="utils_8hpp.xhtml#a8f6fbf8b243a10af40ce8d47a1013384">roundup</a></div><div class="ttdeci">T roundup(const T a, const T b)</div><div class="ttdef"><b>Definition:</b> <a href="utils_8hpp_source.xhtml#l00043">utils.hpp:43</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_convolution_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEWinogradConvolutionLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_8cpp_source.xhtml#l00552">NEWinogradConvolutionLayer.cpp:552</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a9561091f3566e78ad3aa39259bc4126a"><div class="ttname"><a href="_error_8h.xhtml#a9561091f3566e78ad3aa39259bc4126a">ARM_COMPUTE_RETURN_ERROR_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_MSG(...)</div><div class="ttdoc">An error is returned with the given description.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00194">Error.h:194</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00082">IMemoryGroup.h:82</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a></div><div class="ttdoc">Upper Bounded Rectifier ( )</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_a385241dcc5062af6ecac8bdafe01bb2a"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#a385241dcc5062af6ecac8bdafe01bb2a">arm_compute::NEGEMM::configure</a></div><div class="ttdeci">void configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs, output.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00051">NEGEMM.cpp:51</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_activation_layer_xhtml_adfb5ef37594fc9371c4a2b95e3d5e31b"><div class="ttname"><a href="classarm__compute_1_1_n_e_activation_layer.xhtml#adfb5ef37594fc9371c4a2b95e3d5e31b">arm_compute::NEActivationLayer::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, ActivationLayerInfo activation_info)</div><div class="ttdoc">[NEActivationLayer snippet]</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_layer_8cpp_source.xhtml#l00036">NEActivationLayer.cpp:36</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ab1806bf0c5a41f674fb9d2dc6af644f5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ab1806bf0c5a41f674fb9d2dc6af644f5">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a1c69762a42ab8add645d0a949b6f4b1f"><div class="ttname"><a href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_p_p_permute_xhtml_a93c836ab36443b23753d99495761daf7"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_permute.xhtml#a93c836ab36443b23753d99495761daf7">arm_compute::CPPPermute::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const PermutationVector &perm)</div><div class="ttdoc">Configure the permute CPP kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_permute_8cpp_source.xhtml#l00031">CPPPermute.cpp:31</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_p_p_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICPPSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_p_p_simple_function_8cpp_source.xhtml#l00035">ICPPSimpleFunction.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_ac24584a63e484123e3756d1b2a1c9e2f"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#ac24584a63e484123e3756d1b2a1c9e2f">arm_compute::IScheduler::num_threads</a></div><div class="ttdeci">virtual unsigned int num_threads() const =0</div><div class="ttdoc">Returns the number of threads that the SingleThreadScheduler has in his pool.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5f5b6c4337eac9e2e0046ca2304d80dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">ArithmeticAddition.cpp:138</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape & tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00261">TensorInfo.h:261</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00075">Types.h:75</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel_xhtml_a9ede996037a6406aca5217f9ad5e2f28"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#a9ede996037a6406aca5217f9ad5e2f28">arm_compute::NEWinogradLayerTransformOutputKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEWinogradLayerTransform...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_kernel_8cpp_source.xhtml#l00507">NEWinogradConvolutionLayerKernel.cpp:507</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_configuration_xhtml"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_configuration.xhtml">arm_compute::NEWinogradLayerConfiguration</a></div><div class="ttdoc">NEON kernel to perform Winograd.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_convolution_layer_kernel_8h_source.xhtml#l00583">NEWinogradConvolutionLayerKernel.h:583</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div> |
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