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| <div class="title">CLDepthwiseConvolutionLayer.cpp</div> </div> |
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| <a href="_c_l_depthwise_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="_c_l_depthwise_convolution_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.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="_i_c_l_tensor_8h.xhtml">arm_compute/core/CL/ICLTensor.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel_8h.xhtml">arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel_8h.xhtml">arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_pixel_value_8h.xhtml">arm_compute/core/PixelValue.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="_asymm_helpers_8h.xhtml">arm_compute/core/utils/quantization/AsymmHelpers.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_c_l_scheduler_8h.xhtml">arm_compute/runtime/CL/CLScheduler.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="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1misc.xhtml">arm_compute::misc</a>;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml">arm_compute::misc::shape_calculator</a>;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#ac2d0894cb8f94dccd20d71ed5140a70c"> 41</a></span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#ac2d0894cb8f94dccd20d71ed5140a70c">CLDepthwiseConvolutionLayer3x3::CLDepthwiseConvolutionLayer3x3</a>(std::shared_ptr<IMemoryManager> memory_manager)</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  : _memory_group(std::move(memory_manager)), _kernel(nullptr), _border_handler(), _permute_input_to_nchw(), _permute_weights_to_nchw(), _permute_output_to_nhwc(), _reshape_weights(), _permuted_input(),</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  _permuted_weights(), _permuted_output(), _original_weights(nullptr), _needs_permute(false), _needs_weights_reshape(false), _is_prepared(false)</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> }</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#a9aac2e1411d40b334fb323c9b6b913a2"> 47</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#a9aac2e1411d40b334fb323c9b6b913a2">CLDepthwiseConvolutionLayer3x3::configure</a>(<a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</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_c_l_tensor.xhtml">ICLTensor</a> *biases, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output, <span class="keyword">const</span> <a class="code" href="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="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</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="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>)</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// idx_w and idx_h only used for validation</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_w = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</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>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_h = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</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>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(idx_w);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(idx_h);</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>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_w) + (<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>(idx_w) - 1) * (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x() - 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_w) + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right());</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_h) + (<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>(idx_h) - 1) * (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.y() - 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_h) + <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());</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>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_nhwc = 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>() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</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>  _needs_permute = is_nhwc && (depth_multiplier > 1);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  _needs_weights_reshape = is_nhwc && (depth_multiplier == 1)</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  && <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</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="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input_to_use = input;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output_to_use = output;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_stride_1 = ((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().second) && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == 1));</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_dot8_supported = <a class="code" href="namespacearm__compute.xhtml#ac07e02c0066cf540a5a2665fa7d54934">dot8_supported</a>(<a class="code" href="classarm__compute_1_1_c_l_kernel_library.xhtml#acba005f5ce2c62cbf3f94d074d9007aa">CLKernelLibrary::get</a>().get_device());</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_stride_1_dilation_1 = (is_stride_1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x() == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.y() == 1);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="structarm__compute_1_1_depthwise_convolution_reshape_info.xhtml">DepthwiseConvolutionReshapeInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.c0 = 4;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.transpose = is_stride_1_dilation_1 && is_dot8_supported;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_permuted_input);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_permuted_output);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="comment">// Configure the function to transform the input tensor from NHWC -> NCHW</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  _permute_input_to_nchw.<a class="code" href="classarm__compute_1_1_c_l_permute.xhtml#af1f5e1b7e8bbf0768c406be880387a0d">configure</a>(input, &_permuted_input, <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="l00088"></a><span class="lineno"> 88</span>  _permuted_input.<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#a70b6e1495b94818cce4981dbac6bdd66">set_data_layout</a>(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="comment">// Configure the function to transform the weights tensor from HWI -> IHW</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  _permute_weights_to_nchw.<a class="code" href="classarm__compute_1_1_c_l_permute.xhtml#af1f5e1b7e8bbf0768c406be880387a0d">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_permuted_weights, <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="l00092"></a><span class="lineno"> 92</span>  _permuted_weights.<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#a70b6e1495b94818cce4981dbac6bdd66">set_data_layout</a>(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  _permuted_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>());</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  input_to_use = &_permuted_input;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  weights_to_use = &_permuted_weights;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  output_to_use = &_permuted_output;</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>  _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>();</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  }</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(is_nhwc)</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">if</span>(_needs_weights_reshape)</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>  _reshape_weights.<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_kernel.xhtml#a49b7bf6e6b7fc07e9fcbca39c7f77715">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_permuted_weights, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  weights_to_use = &_permuted_weights;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  }</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NHWCKernel>();</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>();</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> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">// Configure kernel</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  _kernel->set_target(<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().target());</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// Permute output if needed</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// Configure the function to transform the convoluted output to ACL's native ordering format NCHW</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  _permuted_output.<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#a70b6e1495b94818cce4981dbac6bdd66">set_data_layout</a>(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  _permute_output_to_nhwc.<a class="code" href="classarm__compute_1_1_c_l_permute.xhtml#af1f5e1b7e8bbf0768c406be880387a0d">configure</a>(&_permuted_output, output, <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="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// Allocate tensors</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  _permuted_input.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  _permuted_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  }</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// Configure border handler</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> &&zero_value(0.f);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()))</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  zero_value = <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a>(static_cast<uint8_t>(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#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>().<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  }</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  _border_handler.<a class="code" href="classarm__compute_1_1_c_l_fill_border_kernel.xhtml#ae1b9fe62ed42f469f1de879c33d75c06">configure</a>(input_to_use, _kernel->border_size(), <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>, zero_value);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> }</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#a87b0b2b008eb501d650caddecf96ee63"> 139</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#a87b0b2b008eb501d650caddecf96ee63">CLDepthwiseConvolutionLayer3x3::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<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="l00140"></a><span class="lineno"> 140</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier, <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>, <a class="code" href="namespacearm__compute.xhtml#a735ac6c2a02e320969625308810444f3">GPUTarget</a> gpu_target, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>)</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> {</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() == <a class="code" href="namespacearm__compute.xhtml#a3a440b3893fa10608d4428958be1c52ea696b031073e74bf2cb98e5ef201d4aa3">DataLayout::UNKNOWN</a>);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_nhwc = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_permute = is_nhwc && (depth_multiplier > 1);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_weights_reshape = is_nhwc && (depth_multiplier == 1) && is_quantized;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_stride_1 = ((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().second) && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == 1));</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_stride_1_dilation_1 = (is_stride_1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x() == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.y() == 1);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_dot8_supported = <a class="code" href="namespacearm__compute.xhtml#ac07e02c0066cf540a5a2665fa7d54934">dot8_supported</a>(<a class="code" href="classarm__compute_1_1_c_l_kernel_library.xhtml#acba005f5ce2c62cbf3f94d074d9007aa">CLKernelLibrary::get</a>().get_device());</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="structarm__compute_1_1_depthwise_convolution_reshape_info.xhtml">DepthwiseConvolutionReshapeInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.c0 = 4;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.transpose = is_stride_1_dilation_1 && is_dot8_supported;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">if</span>(is_quantized)</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">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> iq_info = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> wq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->quantization_info().uniform();</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> oq_info = (output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>() == 0) ? iq_info : output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = iq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> * wq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> / oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(multiplier);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(multiplier > 1.0f);</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> </div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordflow">if</span>(needs_permute)</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>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> permuted_input_shape = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> permuted_weights_shape = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->tensor_shape();</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> permuted_output_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">shape_calculator::compute_depthwise_convolution_shape</a>(*input, *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(permuted_input_shape, <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="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(permuted_weights_shape, <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="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(permuted_output_shape, <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="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> permuted_input = input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> permuted_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> permuted_output = output-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel.xhtml#a46abfb5a58cedce793acc288374afa88">CLDepthwiseConvolutionLayer3x3NCHWKernel::validate</a>(&permuted_input, &permuted_weights, biases, &permuted_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, gpu_target,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(is_nhwc)</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  {</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">if</span>(needs_weights_reshape)</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="keyword">auto</span> reshaped_weights_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3f10bc0f3e2a0126ce8c26e3d6a8fb96">arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_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#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel.xhtml#a0ddd829ec27b6f67b924291fac0381cb">CLDepthwiseConvolutionLayer3x3NHWCKernel::validate</a>(input, &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->set_tensor_shape(reshaped_weights_shape), biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier,</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="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></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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel.xhtml#a0ddd829ec27b6f67b924291fac0381cb">CLDepthwiseConvolutionLayer3x3NHWCKernel::validate</a>(input, <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>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  }</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  {</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel.xhtml#a46abfb5a58cedce793acc288374afa88">CLDepthwiseConvolutionLayer3x3NCHWKernel::validate</a>(input, <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>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, gpu_target, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  }</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>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</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> </div><div class="line"><a name="l00205"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#ad1717410afd0be936c6213a63c8005fb"> 205</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#ad1717410afd0be936c6213a63c8005fb">CLDepthwiseConvolutionLayer3x3::run</a>()</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <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="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  {</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  _permute_input_to_nchw.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_border_handler);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(*_kernel);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">if</span>(_needs_permute)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  _permute_output_to_nhwc.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> }</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 224</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">CLDepthwiseConvolutionLayer3x3::prepare</a>()</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> {</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">if</span>(_needs_permute)</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>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  _permuted_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  _permute_weights_to_nchw.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  _original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</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> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keywordflow">if</span>(_needs_weights_reshape)</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  {</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_needs_permute);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  _permuted_weights.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_reshape_weights);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  _original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  }</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  }</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a0d4e3a90bcdef691edfe09c701088e74"> 249</a></span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a0d4e3a90bcdef691edfe09c701088e74">CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer</a>()</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _activationlayer_function(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(),</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), _is_prepared(false), _is_quantized(false), _is_activationlayer_enabled(false), _original_weights(nullptr),</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  _optimised_function(nullptr)</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> }</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a3e75302bf93c0b3607943daca8ec2ed1"> 256</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a3e75302bf93c0b3607943daca8ec2ed1">CLDepthwiseConvolutionLayer::configure</a>(<a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</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_c_l_tensor.xhtml">ICLTensor</a> *biases, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output, <span class="keyword">const</span> <a class="code" href="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="l00257"></a><span class="lineno"> 257</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier, <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="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>)</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> {</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <a class="code" href="_validate_8h.xhtml#af16d3fc9c85590971cf583683a745fff">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_LAYOUT</a>(input, output);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_w = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</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>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_h = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</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>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_w) + (<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>(idx_w) - 1) * (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x() - 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_w) + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right());</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_h) + (<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>(idx_h) - 1) * (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.y() - 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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_h) + <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());</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>  <span class="keyword">const</span> <span class="keywordtype">bool</span> can_run_optimised_3x3_kernel = (<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>(idx_w) == 3) && (<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>(idx_h) == 3);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">if</span>(<span class="keywordtype">bool</span>(can_run_optimised_3x3_kernel))</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>  <span class="keyword">auto</span> f = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3>();</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  f->configure(input, <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>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  _optimised_function = std::move(f);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  {</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_c = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</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>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> weights_w = <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>(idx_w);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> weights_h = <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>(idx_h);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> weights_z = <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>(idx_c);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  _is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordtype">bool</span> append_bias = (biases != <span class="keyword">nullptr</span>) && !_is_quantized;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a735ac6c2a02e320969625308810444f3">GPUTarget</a> gpu_target = <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a27561688e2fc60176608ef725a4ecb30">target</a>();</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">// Calculate output shape</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">shape_calculator::compute_depthwise_convolution_shape</a>(*input-><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#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="comment">// Output auto inizialitation if not yet initialized</span></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>));</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <a class="code" href="_validate_8h.xhtml#ad9fd47433ba6091668c207e21dd6385f">ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="comment">// Output width and height</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_w];</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_h];</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="comment">// Set up intermediate tensors</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> conv_size = conv_w * conv_h;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> iq_info = 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#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> wq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> oq_info = output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="comment">// Im2Col configuration</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col = input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, patch_size);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, conv_size);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(2, weights_z);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  _input_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(shape_im2col));</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  _im2col_kernel.<a class="code" href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">set_target</a>(gpu_target);</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  _im2col_kernel.<a class="code" href="classarm__compute_1_1_c_l_depthwise_im2_col_kernel.xhtml#a9adc3501bcac4503b2432b77c8564e6b">configure</a>(input, &_input_reshaped, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(weights_w, weights_h), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, append_bias, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">tune_kernel_static</a>(_im2col_kernel);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">// Weights reshape configuration</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_weights_reshape(patch_size, weights_z);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  _weights_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</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#afbc359bde9be72a6edab175978e56662">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(shape_weights_reshape));</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  _weights_reshape_kernel.<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel.xhtml#ab4ce8dea224953952665e2b0d3e0ece7">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_weights_reshaped, append_bias ? biases : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="comment">// GEMV configuration</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> v2mm_dt = (input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>) ? <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>();</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_v2mm_out = input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  shape_v2mm_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, conv_size * weights_z);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  shape_v2mm_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, 1);</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  shape_v2mm_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(2, 1);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  _v2mm_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  _v2mm_kernel.<a class="code" href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">set_target</a>(gpu_target);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  _v2mm_kernel.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel.xhtml#a083cbb32c94ef493098ee169dc36c486">configure</a>(&_input_reshaped, &_weights_reshaped, &_v2mm_output);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">tune_kernel_static</a>(_v2mm_kernel);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  _output_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(_v2mm_output.<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#afbc359bde9be72a6edab175978e56662">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>));</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  _vector_to_tensor_kernel.<a class="code" href="classarm__compute_1_1_c_l_depthwise_vector_to_tensor_kernel.xhtml#a0958dbd0887df9802a2433316e8fc6b7">configure</a>(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output, conv_w, conv_h);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="comment">// Output staged configuration</span></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  {</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> output_quant_info = (output->info()->total_size() == 0) ? iq_info : oq_info;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordtype">int</span> output_multiplier = 0;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordtype">int</span> output_shift = 0;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = iq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> * wq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> / output_quant_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a22032f9cf47deae265eafb65ff55b594">quantization::calculate_quantized_multiplier_less_than_one</a>(multiplier, &output_multiplier, &output_shift);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  _output_stage_kernel.<a class="code" href="classarm__compute_1_1_c_l_direct_convolution_layer_output_stage_kernel.xhtml#a5fcbc12271b2180b57b5a74d421d0448">configure</a>(&_output_reshaped, biases, output, output_multiplier, output_shift, output_quant_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  _output_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  }</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="comment">// Fill borders on inputs</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> zero_in(static_cast<int32_t>(0));</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> zero_w(static_cast<int32_t>(0));</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  {</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  zero_in = <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a>(static_cast<int32_t>(iq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  zero_w = <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a>(static_cast<int32_t>(wq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">offset</a>));</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  }</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="structarm__compute_1_1_border_size.xhtml">BorderSize</a> border_size = _v2mm_kernel.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel.xhtml#a423f9a45a52983b4de5e2b347f4369c7">border_size</a>();</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  _v2mm_input_fill_border.<a class="code" href="classarm__compute_1_1_c_l_fill_border_kernel.xhtml#ae1b9fe62ed42f469f1de879c33d75c06">configure</a>(&_input_reshaped, border_size, <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>, zero_in);</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>  border_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">bottom</a> = 0;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  _v2mm_weights_fill_border.<a class="code" href="classarm__compute_1_1_c_l_fill_border_kernel.xhtml#ae1b9fe62ed42f469f1de879c33d75c06">configure</a>(&_weights_reshaped, border_size, <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>, zero_w);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// Allocate intermediate tensors</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  _input_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  _v2mm_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="comment">//Configure Activation Layer</span></div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  _is_activationlayer_enabled = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled();</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">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="l00376"></a><span class="lineno"> 376</span>  }</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  }</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> }</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00380"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a4bc5903a6da554c4cd38d2b123d30a4f"> 380</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a4bc5903a6da554c4cd38d2b123d30a4f">CLDepthwiseConvolutionLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<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="l00381"></a><span class="lineno"> 381</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier, <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="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>)</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_w = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_h = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_w) + (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_w) - 1) * (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.x() - 1) > input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_w) + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right());</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_h) + (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_h) - 1) * (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>.y() - 1) > input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_h) + <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());</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> can_run_optimised_3x3_kernel = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_w) == 3) && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_h) == 3);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">if</span>(!can_run_optimised_3x3_kernel)</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_c = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</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>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>((input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_c) * depth_multiplier) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_c));</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="keyword">const</span> <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> append_bias = (biases != <span class="keyword">nullptr</span>) && !is_quantized;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">shape_calculator::compute_depthwise_convolution_shape</a>(*input, *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> weights_w = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_w);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> weights_h = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_h);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> weights_z = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_c);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_w];</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_h];</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> conv_size = conv_w * conv_h;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, patch_size);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, conv_size);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(2, weights_z);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_reshaped(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(shape_im2col));</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_im2_col_kernel.xhtml#aac7d3de10148bd078fa2547e91333ae6">CLDepthwiseIm2ColKernel::validate</a>(input, &input_reshaped, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(weights_w, weights_h), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, append_bias, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> </div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_weights_reshape(patch_size, weights_z);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> weights_reshaped(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(shape_weights_reshape));</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel.xhtml#abf6d9f2ce0ee2f7ce645b79260dba556">CLDepthwiseConvolutionLayerReshapeWeightsGenericKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &weights_reshaped, append_bias ? biases : <span class="keyword">nullptr</span>));</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> v2mm_dt = (input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>) ? <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a> : input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>();</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_v2mm_out = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  shape_v2mm_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, conv_size * weights_z);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  shape_v2mm_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, 1);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  shape_v2mm_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(2, 1);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> v2mm_output(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel.xhtml#a0d647d83c8512fa95fa9adb8fb3e0cab">CLGEMMMatrixVectorMultiplyKernel::validate</a>(&input_reshaped, &weights_reshaped, &v2mm_output));</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> output_reshaped(v2mm_output.clone()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>));</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_vector_to_tensor_kernel.xhtml#afec32bb9280b58786b78b36076343894">CLDepthwiseVectorToTensorKernel::validate</a>(&v2mm_output, (is_quantized) ? &output_reshaped : output, conv_w, conv_h));</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  {</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> iq_info = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>().<a class="code" href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">uniform</a>();</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> wq_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->quantization_info().uniform();</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml">UniformQuantizationInfo</a> oq_info = (output->total_size() == 0) ? iq_info : output->quantization_info().uniform();</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> multiplier = iq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> * wq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a> / oq_info.<a class="code" href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">scale</a>;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(multiplier);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(multiplier > 1.0f);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_direct_convolution_layer_output_stage_kernel.xhtml#ae2d1f3ac8abfab13e554dd6311566540">CLDirectConvolutionLayerOutputStageKernel::validate</a>(&output_reshaped, biases, output));</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  }</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> </div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="comment">// Validate Activation Layer</span></div><div class="line"><a name="l00444"></a><span class="lineno"> 444</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="l00445"></a><span class="lineno"> 445</span>  {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">CLActivationLayer::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="l00447"></a><span class="lineno"> 447</span>  }</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  }</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  {</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#a87b0b2b008eb501d650caddecf96ee63">CLDepthwiseConvolutionLayer3x3::validate</a>(input, <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>, depth_multiplier, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <a class="code" href="namespacearm__compute.xhtml#a735ac6c2a02e320969625308810444f3afa362f4daec88442a387ff7cda411a38">GPUTarget::MIDGARD</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  }</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 456</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">CLDepthwiseConvolutionLayer::run</a>()</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> {</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</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>  <span class="keywordflow">if</span>(_optimised_function != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  _optimised_function->run();</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="keywordflow">else</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  {</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_im2col_kernel);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_v2mm_input_fill_border);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_v2mm_kernel);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_vector_to_tensor_kernel);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  {</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_output_stage_kernel);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  }</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">if</span>(_is_activationlayer_enabled)</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  {</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  _activationlayer_function.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</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>  }</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> }</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 481</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">CLDepthwiseConvolutionLayer::prepare</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>  <span class="keywordflow">if</span>(_optimised_function != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  {</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  _optimised_function->prepare();</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  }</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  {</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  {</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> </div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="comment">// Run weights reshaping and mark original weights tensor as unused</span></div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  _weights_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_weights_reshape_kernel);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_v2mm_weights_fill_border);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  _original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">queue</a>().finish();</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  _is_prepared = <span class="keyword">true</span>;</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>  }</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> }</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="_pixel_value_8h_xhtml"><div class="ttname"><a href="_pixel_value_8h.xhtml">PixelValue.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml">arm_compute::PixelValue</a></div><div class="ttdoc">Class describing the value of a pixel for any image format.</div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00034">PixelValue.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel_xhtml_ab4ce8dea224953952665e2b0d3e0ece7"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel.xhtml#ab4ce8dea224953952665e2b0d3e0ece7">arm_compute::CLDepthwiseConvolutionLayerReshapeWeightsGenericKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *biases=nullptr)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel_8cpp_source.xhtml#l00070">CLDepthwiseConvolutionLayerReshapeWeightsGenericKernel.cpp:70</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_xhtml_aa37e2d0b4cd4f835bfa2a2df4a0bdd2c"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#aa37e2d0b4cd4f835bfa2a2df4a0bdd2c">arm_compute::CLActivationLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLActivationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_8cpp_source.xhtml#l00039">CLActivationLayer.cpp:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a22032f9cf47deae265eafb65ff55b594"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a22032f9cf47deae265eafb65ff55b594">arm_compute::quantization::calculate_quantized_multiplier_less_than_one</a></div><div class="ttdeci">arm_compute::Status calculate_quantized_multiplier_less_than_one(float multiplier, int *quant_multiplier, int *right_shift)</div><div class="ttdoc">Calculate quantized representation of multiplier with value less than one.</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00035">AsymmHelpers.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLDepthwiseConvolutionLayer3x3::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00205">CLDepthwiseConvolutionLayer.cpp:205</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel_xhtml_a0ddd829ec27b6f67b924291fac0381cb"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel.xhtml#a0ddd829ec27b6f67b924291fac0381cb">arm_compute::CLDepthwiseConvolutionLayer3x3NHWCKernel::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, unsigned int depth_multiplier, ActivationLayerInfo act_info=ActivationLayerInfo(), const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDepthwiseConvolutionLa...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel_8cpp_source.xhtml#l00312">CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp:312</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ac07e02c0066cf540a5a2665fa7d54934"><div class="ttname"><a href="namespacearm__compute.xhtml#ac07e02c0066cf540a5a2665fa7d54934">arm_compute::dot8_supported</a></div><div class="ttdeci">bool dot8_supported(const cl::Device &device)</div><div class="ttdoc">Helper function to check whether the cl_arm_integer_dot_product_int8 extension is supported.</div><div class="ttdef"><b>Definition:</b> <a href="core_2_c_l_2_c_l_helpers_8cpp_source.xhtml#l00149">CLHelpers.cpp:149</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLDepthwiseConvolutionLayer3x3::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="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00224">CLDepthwiseConvolutionLayer.cpp:224</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#l00306">TensorInfo.cpp:306</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLDepthwiseConvolutionLayer::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="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00481">CLDepthwiseConvolutionLayer.cpp:481</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel_xhtml_a423f9a45a52983b4de5e2b347f4369c7"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel.xhtml#a423f9a45a52983b4de5e2b347f4369c7">arm_compute::CLGEMMMatrixVectorMultiplyKernel::border_size</a></div><div class="ttdeci">BorderSize border_size() const override</div><div class="ttdoc">The size of the border for that kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_matrix_vector_multiply_kernel_8cpp_source.xhtml#l00078">CLGEMMMatrixVectorMultiplyKernel.cpp:78</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ac7147815227e7ba91814cfdcd38f23ed"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape</a></div><div class="ttdeci">TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info, unsigned int depth_multiplier, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Calculate the depthwise convolution output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00410">ShapeCalculator.h:410</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel_xhtml_abf6d9f2ce0ee2f7ce645b79260dba556"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel.xhtml#abf6d9f2ce0ee2f7ce645b79260dba556">arm_compute::CLDepthwiseConvolutionLayerReshapeWeightsGenericKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *biases=nullptr)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDepthwiseConvolutionLa...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_reshape_weights_generic_kernel_8cpp_source.xhtml#l00102">CLDepthwiseConvolutionLayerReshapeWeightsGenericKernel.cpp:102</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">ConvolutionLayer.cpp:175</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a5befbfaf6bc224eabc58b5e88b1de6d1"><div class="ttname"><a href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00543">Validate.h:543</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_xhtml_ac2d0894cb8f94dccd20d71ed5140a70c"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#ac2d0894cb8f94dccd20d71ed5140a70c">arm_compute::CLDepthwiseConvolutionLayer3x3::CLDepthwiseConvolutionLayer3x3</a></div><div class="ttdeci">CLDepthwiseConvolutionLayer3x3(std::shared_ptr< IMemoryManager > memory_manager=nullptr)</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00041">CLDepthwiseConvolutionLayer.cpp:41</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml">arm_compute::BorderSize</a></div><div class="ttdoc">Container for 2D border size.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00259">Types.h:259</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_xhtml_a87b0b2b008eb501d650caddecf96ee63"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#a87b0b2b008eb501d650caddecf96ee63">arm_compute::CLDepthwiseConvolutionLayer3x3::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, unsigned int depth_multiplier=1, ActivationLayerInfo act_info=ActivationLayerInfo(), GPUTarget gpu_target=GPUTarget::MIDGARD, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDepthwiseConvolutionLa...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00139">CLDepthwiseConvolutionLayer.cpp:139</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_direct_convolution_layer_output_stage_kernel_xhtml_a5fcbc12271b2180b57b5a74d421d0448"><div class="ttname"><a href="classarm__compute_1_1_c_l_direct_convolution_layer_output_stage_kernel.xhtml#a5fcbc12271b2180b57b5a74d421d0448">arm_compute::CLDirectConvolutionLayerOutputStageKernel::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, const ICLTensor *bias=nullptr, ICLTensor *output=nullptr, int result_fixedpoint_multiplier=0, int result_shift=0, int result_offset_after_shift=0)</div><div class="ttdoc">Set the accumulate buffer and the biases of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_direct_convolution_output_stage_kernel_8cpp_source.xhtml#l00129">CLDirectConvolutionOutputStageKernel.cpp:129</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a27561688e2fc60176608ef725a4ecb30"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a27561688e2fc60176608ef725a4ecb30">arm_compute::CLScheduler::target</a></div><div class="ttdeci">GPUTarget target() const</div><div class="ttdoc">Get the target GPU.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00112">CLScheduler.h:112</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00599">Winograd.cpp:599</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00193">Error.h:193</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00223">TensorInfo.h:223</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ac74736e3863207232a23b7181c1d0f44"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">arm_compute::TensorInfo::quantization_info</a></div><div class="ttdeci">QuantizationInfo quantization_info() const override</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00293">TensorInfo.h:293</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="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#l00047">Types.h:47</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_kernel_library_xhtml_acba005f5ce2c62cbf3f94d074d9007aa"><div class="ttname"><a href="classarm__compute_1_1_c_l_kernel_library.xhtml#acba005f5ce2c62cbf3f94d074d9007aa">arm_compute::CLKernelLibrary::get</a></div><div class="ttdeci">static CLKernelLibrary & get()</div><div class="ttdoc">Access the KernelLibrary singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_kernel_library_8cpp_source.xhtml#l01037">CLKernelLibrary.cpp:1037</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="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml">arm_compute::UniformQuantizationInfo</a></div><div class="ttdoc">Quantization info when assuming per layer quantization.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00039">QuantizationInfo.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_im2_col_kernel_xhtml_aac7d3de10148bd078fa2547e91333ae6"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_im2_col_kernel.xhtml#aac7d3de10148bd078fa2547e91333ae6">arm_compute::CLDepthwiseIm2ColKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDepthwiseIm2ColKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_im2_col_kernel_8cpp_source.xhtml#l00113">CLDepthwiseIm2ColKernel.cpp:113</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a802ffcf1b49237efe5be8a314d3f3869"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">arm_compute::BorderSize::bottom</a></div><div class="ttdeci">unsigned int bottom</div><div class="ttdoc">bottom of the border</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00341">Types.h:341</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml_a1d28dec57cce925ad92342891bd71e7c"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml#a1d28dec57cce925ad92342891bd71e7c">arm_compute::UniformQuantizationInfo::scale</a></div><div class="ttdeci">float scale</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00061">QuantizationInfo.h:61</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="structarm__compute_1_1_depthwise_convolution_reshape_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_depthwise_convolution_reshape_info.xhtml">arm_compute::DepthwiseConvolutionReshapeInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01829">Types.h:1829</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a3f10bc0f3e2a0126ce8c26e3d6a8fb96"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3f10bc0f3e2a0126ce8c26e3d6a8fb96">arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_shape</a></div><div class="ttdeci">TensorShape compute_reshaped_depthwise_weights_shape(const ITensorInfo &input, const DepthwiseConvolutionReshapeInfo &info)</div><div class="ttdoc">Calculate the reshaped shape of the weights to use in depthwise convolution.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00261">ShapeCalculator.h:261</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01517">Types.h:1517</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_fill_border_kernel_xhtml_ae1b9fe62ed42f469f1de879c33d75c06"><div class="ttname"><a href="classarm__compute_1_1_c_l_fill_border_kernel.xhtml#ae1b9fe62ed42f469f1de879c33d75c06">arm_compute::CLFillBorderKernel::configure</a></div><div class="ttdeci">void configure(ICLTensor *tensor, BorderSize border_size, BorderMode border_mode, const PixelValue &constant_border_value=PixelValue())</div><div class="ttdoc">Initialise the kernel's input, output and border mode.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_fill_border_kernel_8cpp_source.xhtml#l00062">CLFillBorderKernel.cpp:62</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00201">Helpers.inl:201</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a78839e7861ba8ffed52ca55da2745761"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">arm_compute::TensorInfo::set_quantization_info</a></div><div class="ttdeci">ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info) override</div><div class="ttdoc">Set the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00364">TensorInfo.cpp:364</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a21c3e11887f3acf9284ca763372c7da0"><div class="ttname"><a href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a></div><div class="ttdeci">void permute(Dimensions< T > &dimensions, const PermutationVector &perm)</div><div class="ttdoc">Permutes given Dimensions according to a permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00570">Helpers.h:570</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="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div> |
| <div class="ttc" id="_c_l_scheduler_8h_xhtml"><div class="ttname"><a href="_c_l_scheduler_8h.xhtml">CLScheduler.h</a></div></div> |
| <div class="ttc" id="_c_l_depthwise_convolution_layer_8h_xhtml"><div class="ttname"><a href="_c_l_depthwise_convolution_layer_8h.xhtml">CLDepthwiseConvolutionLayer.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_vector_to_tensor_kernel_xhtml_afec32bb9280b58786b78b36076343894"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_vector_to_tensor_kernel.xhtml#afec32bb9280b58786b78b36076343894">arm_compute::CLDepthwiseVectorToTensorKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDepthwiseVectorToTenso...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_vector_to_tensor_kernel_8cpp_source.xhtml#l00094">CLDepthwiseVectorToTensorKernel.cpp:94</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a70b6e1495b94818cce4981dbac6bdd66"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a70b6e1495b94818cce4981dbac6bdd66">arm_compute::TensorInfo::set_data_layout</a></div><div class="ttdeci">ITensorInfo & set_data_layout(const DataLayout &data_layout) override</div><div class="ttdoc">Set the data layout of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00370">TensorInfo.cpp:370</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_xhtml_a9aac2e1411d40b334fb323c9b6b913a2"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3.xhtml#a9aac2e1411d40b334fb323c9b6b913a2">arm_compute::CLDepthwiseConvolutionLayer3x3::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier=1, ActivationLayerInfo act_info=ActivationLayerInfo(), const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Initialize the function's source, destination, conv and border_size.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00047">CLDepthwiseConvolutionLayer.cpp:47</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#l00160">Error.h:160</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</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_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::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_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction.cpp:37</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_im2_col_kernel_xhtml_a9adc3501bcac4503b2432b77c8564e6b"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_im2_col_kernel.xhtml#a9adc3501bcac4503b2432b77c8564e6b">arm_compute::CLDepthwiseIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias=false, unsigned int depth_multiplier=1, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_im2_col_kernel_8cpp_source.xhtml#l00066">CLDepthwiseIm2ColKernel.cpp:66</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml_a706fc156bcd4c45441bcaad05884b57d"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml#a706fc156bcd4c45441bcaad05884b57d">arm_compute::QuantizationInfo::uniform</a></div><div class="ttdeci">UniformQuantizationInfo uniform() const</div><div class="ttdoc">Return per layer quantization info.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00134">QuantizationInfo.h:134</a></div></div> |
| <div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1misc.xhtml">arm_compute::misc</a></div><div class="ttdef"><b>Definition:</b> <a href="_c_r_t_p_8h_source.xhtml#l00029">CRTP.h:29</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_ad9fd47433ba6091668c207e21dd6385f"><div class="ttname"><a href="_validate_8h.xhtml#ad9fd47433ba6091668c207e21dd6385f">ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00286">Validate.h:286</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00676">Types.h:676</a></div></div> |
| <div class="ttc" id="_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel_8h_xhtml"><div class="ttname"><a href="_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel_8h.xhtml">CLDepthwiseConvolutionLayer3x3NCHWKernel.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a3f3e1a3200223e6a304a533b1016e749"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">arm_compute::ITensorInfo::quantization_info</a></div><div class="ttdeci">virtual QuantizationInfo quantization_info() const =0</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ae1a643e517f50bf0392fb6516dd7cf67"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">arm_compute::CLScheduler::enqueue</a></div><div class="ttdeci">void enqueue(ICLKernel &kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler.cpp:95</a></div></div> |
| <div class="ttc" id="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="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00789">Validate.h:789</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ad381d1aed28b4b1e1f5a710633934580"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">arm_compute::CLScheduler::queue</a></div><div class="ttdeci">cl::CommandQueue & queue()</div><div class="ttdoc">Accessor for the associated CL command queue.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00102">CLScheduler.h:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_kernel_xhtml_a49b7bf6e6b7fc07e9fcbca39c7f77715"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer_reshape_weights_kernel.xhtml#a49b7bf6e6b7fc07e9fcbca39c7f77715">arm_compute::CLDepthwiseConvolutionLayerReshapeWeightsKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const DepthwiseConvolutionReshapeInfo &info)</div><div class="ttdoc">Initialize the function's source and destination.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_reshape_weights_kernel_8cpp_source.xhtml#l00087">CLDepthwiseConvolutionLayerReshapeWeightsKernel.cpp:87</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01030">Utils.h:1030</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel_xhtml_a0d647d83c8512fa95fa9adb8fb3e0cab"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel.xhtml#a0d647d83c8512fa95fa9adb8fb3e0cab">arm_compute::CLGEMMMatrixVectorMultiplyKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMMMatrixVectorMultip...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_matrix_vector_multiply_kernel_8cpp_source.xhtml#l00128">CLGEMMMatrixVectorMultiplyKernel.cpp:128</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_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel_xhtml_a46abfb5a58cedce793acc288374afa88"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel.xhtml#a46abfb5a58cedce793acc288374afa88">arm_compute::CLDepthwiseConvolutionLayer3x3NCHWKernel::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, unsigned int depth_multiplier, ActivationLayerInfo act_info=ActivationLayerInfo(), GPUTarget gpu_target=GPUTarget::MIDGARD, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDepthwiseConvolutionLa...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer3x3_n_c_h_w_kernel_8cpp_source.xhtml#l00326">CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp:326</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</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#l00046">IMemoryGroup.h:46</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_tensor.xhtml">arm_compute::ICLTensor</a></div><div class="ttdoc">Interface for OpenCL tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_tensor_8h_source.xhtml#l00042">ICLTensor.h:42</a></div></div> |
| <div class="ttc" id="_i_c_l_tensor_8h_xhtml"><div class="ttname"><a href="_i_c_l_tensor_8h.xhtml">ICLTensor.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a18064e0011c3869d884653e9e7c47b66"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">arm_compute::ITensorInfo::total_size</a></div><div class="ttdeci">virtual size_t total_size() const =0</div><div class="ttdoc">Returns the total size of the tensor in bytes.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_xhtml_a3e75302bf93c0b3607943daca8ec2ed1"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a3e75302bf93c0b3607943daca8ec2ed1">arm_compute::CLDepthwiseConvolutionLayer::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier=1, const ActivationLayerInfo &act_info=ActivationLayerInfo(), const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Initialize the function's source, destination, weights and convolution information.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00256">CLDepthwiseConvolutionLayer.cpp:256</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">arm_compute::PaddingMode::CONSTANT</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a735ac6c2a02e320969625308810444f3"><div class="ttname"><a href="namespacearm__compute.xhtml#a735ac6c2a02e320969625308810444f3">arm_compute::GPUTarget</a></div><div class="ttdeci">GPUTarget</div><div class="ttdoc">Available GPU Targets.</div><div class="ttdef"><b>Definition:</b> <a href="_g_p_u_target_8h_source.xhtml#l00034">GPUTarget.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a3a440b3893fa10608d4428958be1c52ea696b031073e74bf2cb98e5ef201d4aa3"><div class="ttname"><a href="namespacearm__compute.xhtml#a3a440b3893fa10608d4428958be1c52ea696b031073e74bf2cb98e5ef201d4aa3">arm_compute::CLVersion::UNKNOWN</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a735ac6c2a02e320969625308810444f3afa362f4daec88442a387ff7cda411a38"><div class="ttname"><a href="namespacearm__compute.xhtml#a735ac6c2a02e320969625308810444f3afa362f4daec88442a387ff7cda411a38">arm_compute::GPUTarget::MIDGARD</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_direct_convolution_layer_output_stage_kernel_xhtml_ae2d1f3ac8abfab13e554dd6311566540"><div class="ttname"><a href="classarm__compute_1_1_c_l_direct_convolution_layer_output_stage_kernel.xhtml#ae2d1f3ac8abfab13e554dd6311566540">arm_compute::CLDirectConvolutionLayerOutputStageKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias=nullptr, const ITensorInfo *output=nullptr)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDirectConvolutionLayer...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_direct_convolution_output_stage_kernel_8cpp_source.xhtml#l00174">CLDirectConvolutionOutputStageKernel.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLDepthwiseConvolutionLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00456">CLDepthwiseConvolutionLayer.cpp:456</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</div></div> |
| <div class="ttc" id="structarm__compute_1_1_uniform_quantization_info_xhtml_a97bd6c077f3c7769f575b82988b9b668"><div class="ttname"><a href="structarm__compute_1_1_uniform_quantization_info.xhtml#a97bd6c077f3c7769f575b82988b9b668">arm_compute::UniformQuantizationInfo::offset</a></div><div class="ttdeci">int32_t offset</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00062">QuantizationInfo.h:62</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_activation_layer_xhtml_a239fea32ba46d038ba350dba58026c45"><div class="ttname"><a href="classarm__compute_1_1_c_l_activation_layer.xhtml#a239fea32ba46d038ba350dba58026c45">arm_compute::CLActivationLayer::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfo act_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_activation_layer_8cpp_source.xhtml#l00032">CLActivationLayer.cpp:32</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_xhtml_a4bc5903a6da554c4cd38d2b123d30a4f"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a4bc5903a6da554c4cd38d2b123d30a4f">arm_compute::CLDepthwiseConvolutionLayer::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, unsigned int depth_multiplier=1, const ActivationLayerInfo &act_info=ActivationLayerInfo(), const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLDepthwiseConvolutionLa...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00380">CLDepthwiseConvolutionLayer.cpp:380</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_depthwise_convolution_layer_xhtml_a0d4e3a90bcdef691edfe09c701088e74"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_convolution_layer.xhtml#a0d4e3a90bcdef691edfe09c701088e74">arm_compute::CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer</a></div><div class="ttdeci">CLDepthwiseConvolutionLayer()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_convolution_layer_8cpp_source.xhtml#l00249">CLDepthwiseConvolutionLayer.cpp:249</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a9c54fb6cea3557692fe7c00c40bb40ad"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">arm_compute::TensorShape::set</a></div><div class="ttdeci">TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00078">TensorShape.h:78</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="_validate_8h_xhtml_af16d3fc9c85590971cf583683a745fff"><div class="ttname"><a href="_validate_8h.xhtml#af16d3fc9c85590971cf583683a745fff">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_LAYOUT</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00492">Validate.h:492</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml">arm_compute::misc::shape_calculator</a></div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00040">ShapeCalculator.h:40</a></div></div> |
| <div class="ttc" id="_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel_8h_xhtml"><div class="ttname"><a href="_c_l_depthwise_convolution_layer3x3_n_h_w_c_kernel_8h.xhtml">CLDepthwiseConvolutionLayer3x3NHWCKernel.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_kernel_xhtml_ad5ba9d34a3a855bf1dd2e36316ff550a"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_kernel.xhtml#ad5ba9d34a3a855bf1dd2e36316ff550a">arm_compute::ICLKernel::set_target</a></div><div class="ttdeci">void set_target(GPUTarget target)</div><div class="ttdoc">Set the targeted GPU architecture.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_kernel_8h_source.xhtml#l00271">ICLKernel.h:271</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_permute_xhtml_af1f5e1b7e8bbf0768c406be880387a0d"><div class="ttname"><a href="classarm__compute_1_1_c_l_permute.xhtml#af1f5e1b7e8bbf0768c406be880387a0d">arm_compute::CLPermute::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const PermutationVector &perm)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_permute_8cpp_source.xhtml#l00033">CLPermute.cpp:33</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#l00326">Helpers.inl:326</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="_asymm_helpers_8h_xhtml"><div class="ttname"><a href="_asymm_helpers_8h.xhtml">AsymmHelpers.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a6c2059df991a75abef4eb643510c9544"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">arm_compute::CLScheduler::tune_kernel_static</a></div><div class="ttdeci">void tune_kernel_static(ICLKernel &kernel)</div><div class="ttdoc">Tunes OpenCL kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00172">CLScheduler.h:172</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#l00074">Types.h:74</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel_xhtml_a083cbb32c94ef493098ee169dc36c486"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m_matrix_vector_multiply_kernel.xhtml#a083cbb32c94ef493098ee169dc36c486">arm_compute::CLGEMMMatrixVectorMultiplyKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_matrix_vector_multiply_kernel_8cpp_source.xhtml#l00083">CLGEMMMatrixVectorMultiplyKernel.cpp:83</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_c_l_depthwise_vector_to_tensor_kernel_xhtml_a0958dbd0887df9802a2433316e8fc6b7"><div class="ttname"><a href="classarm__compute_1_1_c_l_depthwise_vector_to_tensor_kernel.xhtml#a0958dbd0887df9802a2433316e8fc6b7">arm_compute::CLDepthwiseVectorToTensorKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, size_t conv_w, size_t conv_h)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_depthwise_vector_to_tensor_kernel_8cpp_source.xhtml#l00064">CLDepthwiseVectorToTensorKernel.cpp:64</a></div></div> |
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