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| <a href="_n_e_im2_col_kernel_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_n_e_im2_col_kernel_8h.xhtml">arm_compute/core/NEON/kernels/NEIm2ColKernel.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="_c_p_p_2_validate_8h.xhtml">arm_compute/core/CPP/Validate.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_i_tensor_8h.xhtml">arm_compute/core/ITensor.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_size2_d_8h.xhtml">arm_compute/core/Size2D.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_tensor_info_8h.xhtml">arm_compute/core/TensorInfo.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_validate_8h.xhtml">arm_compute/core/Validate.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</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="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <arm_neon.h></span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <cstddef></span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <cstdint></span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <cstring></span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include <tuple></span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword">using namespace </span>misc::shape_calculator;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> validate_arguments(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &kernel_dims, <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="l00049"></a><span class="lineno"> 49</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</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>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>)</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a>(input);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_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="l00053"></a><span class="lineno"> 53</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#a7cfb31af63202568efef5214acfbf3ba">data_type</a>() == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a> && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</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#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="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> > 1, <span class="stringliteral">"Number of groups greater than one are not supported on NEON"</span>);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">if</span>(output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>() > 0)</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  {</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> expected_output = output-><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_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7">compute_im2col_conv_shape</a>(input, kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <span class="keyword">false</span>));</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(&expected_output, output);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, output);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a>(input, output);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  }</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> std::pair<Status, Window> <a class="code" href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">validate_and_configure_window</a>(<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <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_size2_d.xhtml">Size2D</a> &kernel_dims, <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="l00069"></a><span class="lineno"> 69</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</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="l00070"></a><span class="lineno"> 70</span> {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(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="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(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="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(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="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::pair<unsigned int, unsigned int> convolved_dims = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(width_idx), input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(height_idx),</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="comment">// Output tensor auto initialization if not yet initialized</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output, input-><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_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7">compute_im2col_conv_shape</a>(input, kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <span class="keyword">false</span>)));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> win = <a class="code" href="namespacearm__compute.xhtml#ab7980fa5ee693e3282a76da047a1c3b5">calculate_max_window</a>(*input, <a class="code" href="classarm__compute_1_1_steps.xhtml">Steps</a>());</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  win.set(width_idx, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, convolved_dims.first, 1));</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  win.set(height_idx, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, convolved_dims.second, 1));</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  win.set(channel_idx, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 1, 1));</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="comment">// The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a9586081a29fceb532ab270bd843abee6">set_valid_region</a>(<a class="code" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(), output-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</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="keywordflow">return</span> std::make_pair(<a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{}, win);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> }</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">bool</span> has_pads></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> linearize_volume_nchw(<span class="keyword">const</span> uint8_t *<span class="keyword">const</span> in_ptr,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  T *out_ptr,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">int</span> top_left_x,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordtype">int</span> top_left_y,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordtype">int</span> kernel_width,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordtype">int</span> kernel_height,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordtype">int</span> kernel_depth,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordtype">int</span> input_w,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordtype">int</span> input_h,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordtype">int</span> input_stride_x,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordtype">int</span> input_stride_y,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordtype">int</span> input_stride_z,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordtype">int</span> pad_value,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordtype">int</span> dilation_x,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordtype">int</span> dilation_y)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> kernel_size2 = kernel_width * kernel_height;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> x_e = top_left_x + kernel_width * dilation_x;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> y_e = top_left_y + kernel_height * dilation_y;</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">// Linearize volume</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordtype">int</span> d = 0;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// This for loop linearize a volume with 3 slices. This allows:</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// 1) to reduce the iterations of the outer for loop "d"</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">for</span>(; d <= (kernel_depth - 3); d += 3)</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="keywordflow">for</span>(<span class="keywordtype">int</span> y = top_left_y; y < y_e; y += dilation_y)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">if</span>((y < 0 || y >= input_h) && has_pads)</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">// All the values will be the offset (will be zeros when not quantized)</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = top_left_x; x < x_e; x += dilation_x, ++out_ptr)</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  *(out_ptr + 0 * kernel_size2) = pad_value;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  *(out_ptr + 1 * kernel_size2) = pad_value;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  *(out_ptr + 2 * kernel_size2) = pad_value;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">else</span></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>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = top_left_x; x < x_e; x += dilation_x, ++out_ptr)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  {</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">if</span>((x < 0 || x >= input_w) && has_pads)</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  *(out_ptr + 0 * kernel_size2) = pad_value;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  *(out_ptr + 1 * kernel_size2) = pad_value;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  *(out_ptr + 2 * kernel_size2) = pad_value;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  {</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x)));</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x)));</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x)));</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  }</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  out_ptr += 2 * kernel_size2;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  }</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="comment">// Left over</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">for</span>(; d < kernel_depth; d++)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = top_left_y; y < y_e; y += dilation_y)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  {</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">if</span>((y < 0 || y >= input_h) && has_pads)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">// All the values will be the offset (will be zeros when not quantized)</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  memset(out_ptr, pad_value, kernel_width * <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  out_ptr += kernel_width;</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">else</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  {</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = top_left_x; x < x_e; x += dilation_x, ++out_ptr)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">if</span>((x < 0 || x >= input_w) && has_pads)</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>  *out_ptr = pad_value;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">else</span></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>  *out_ptr = *(reinterpret_cast<const T *>(in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x)));</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  }</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="comment">// Append 1 if the convolution layer has biases</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  *out_ptr = static_cast<T>(1);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  }</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> }</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">bool</span> has_pads></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> linearize_volume_nhwc(<span class="keyword">const</span> uint8_t *<span class="keyword">const</span> in_ptr,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  T *out_ptr,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordtype">int</span> start_x,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordtype">int</span> start_y,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordtype">int</span> kernel_width,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordtype">int</span> kernel_height,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordtype">int</span> input_w,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordtype">int</span> input_h,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordtype">int</span> input_c,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordtype">int</span> input_stride_y,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordtype">int</span> input_stride_z,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordtype">int</span> pad_value,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordtype">int</span> dilation_x,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordtype">int</span> dilation_y)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> end_x = start_x + kernel_width * dilation_x;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> end_y = start_y + kernel_height * dilation_y;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_quant = kernel_width * input_c;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> element_size = static_cast<int>(<span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">if</span>((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && (input_stride_y == input_c * element_size))</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = start_y; y < end_y; y += dilation_y)</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="comment">//optimized for no dilation and no boundary pixels</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  out_ptr += input_c * kernel_width;</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>  }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">else</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>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = start_y; y < end_y; y += dilation_y)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">if</span>(y < 0 || y >= input_h)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  {</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  memset(out_ptr, pad_value, pad_quant * element_size);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  out_ptr += pad_quant;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = start_x; x < end_x; x += dilation_x)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  {</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">if</span>(x < 0 || x >= input_w)</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>  memset(out_ptr, pad_value, input_c * element_size);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  out_ptr += input_c;</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  {</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * element_size);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  out_ptr += input_c;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  }</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  }</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  }</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">else</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>  <span class="comment">//optimized for no dilation and no boundary pixels</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  out_ptr += input_c * kernel_width;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  }</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  }</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="comment">// Append 1 if the convolution layer has biases</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>)</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  {</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  *out_ptr = static_cast<T>(1);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> }</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">bool</span> has_pads, <span class="keywordtype">bool</span> is_nchw></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="keywordtype">void</span> NEIm2ColKernel::run_im2col(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window)</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <a class="code" href="_validate_8h.xhtml#a1b35b0d258183cf9ef36adf684d0b88c">ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL</a>(<span class="keyword">this</span>);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <a class="code" href="_validate_8h.xhtml#a6eb9ce82815fe429250189da7592ba75">ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">INEKernel::window</a>(), window);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = _input->info()->data_layout();</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_w = _input->info()->dimension(width_idx);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_h = _input->info()->dimension(height_idx);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_c = _input->info()->dimension(channel_idx);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_stride_x = _input->info()->strides_in_bytes().x();</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_stride_y = _input->info()->strides_in_bytes().y();</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> input_stride_z = _input->info()->strides_in_bytes().z();</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_left = _conv_info.pad_left();</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_top = _conv_info.pad_top();</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> stride_x = _conv_info.stride().first;</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> stride_y = _conv_info.stride().second;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_value = <a class="code" href="namespacearm__compute.xhtml#a0bee325b210f81bb89fe1f9e15badf9c">is_data_type_quantized</a>(_input->info()->data_type()) ? _input->info()->quantization_info().uniform().offset : 0;</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>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window_in_out(window);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// The first three dimensions of the input and output are increased by the inner loops</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  window_in_out.set(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 0, 0));</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  window_in_out.set(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 0, 0));</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  window_in_out.set(<a class="code" href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">Window::DimZ</a>, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 0, 0));</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// Create iterators</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> in(_input, window_in_out);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> out(_output, window_in_out);</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>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  {</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> start_w = <span class="keywordtype">id</span>[width_idx] * stride_x - pad_left;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> start_h = <span class="keywordtype">id</span>[height_idx] * stride_y - pad_top;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="comment">// Get pointers</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">const</span> uint8_t *<span class="keyword">const</span> input_ptr = in.ptr();</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">auto</span> output_ptr = reinterpret_cast<T *>(out.ptr() + (<span class="keywordtype">id</span>[width_idx] + <span class="keywordtype">id</span>[height_idx] * _convolved_dims.first) * _output->info()->strides_in_bytes().y());</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="comment">// Linearize volume</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">if</span>(is_nchw)</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>  linearize_volume_nchw<T, has_pads>(input_ptr,</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  output_ptr,</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  _has_bias,</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  start_w,</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  start_h,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  _kernel_width,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  _kernel_height,</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  input_c,</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  input_w,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  input_h,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  input_stride_x,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  input_stride_y,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  input_stride_z,</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  pad_value,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  _dilation.x(),</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  _dilation.y());</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  }</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">else</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>  linearize_volume_nhwc<T, has_pads>(input_ptr,</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  output_ptr,</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  _has_bias,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  start_w,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  start_h,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  _kernel_width,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  _kernel_height,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  input_w,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  input_h,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  input_c,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  input_stride_y,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  input_stride_z,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  pad_value,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  _dilation.x(),</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  _dilation.y());</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>  },</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  in, out);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#aec5fb874227c941e8ac14de3d29e543b"> 346</a></span> <a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#aec5fb874227c941e8ac14de3d29e543b">NEIm2ColKernel::NEIm2ColKernel</a>()</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false), _dilation(1<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>, 1<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>)</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> }</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a585edc13576fe5f51f7cc493751fef52"> 351</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a585edc13576fe5f51f7cc493751fef52">NEIm2ColKernel::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &kernel_dims, <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="l00352"></a><span class="lineno"> 352</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</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>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>)</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> {</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input, output);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(validate_arguments(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  _input = input;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  _output = output;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  _conv_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  _kernel_width = kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  _kernel_height = kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  _dilation = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  _convolved_dims = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(width_idx), 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>(height_idx),</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  _kernel_width, _kernel_height,</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  _conv_info, _dilation);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  _has_bias = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>;</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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordflow">switch</span>(_input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  {</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  _func = (!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.has_padding()) ? &NEIm2ColKernel::run_im2col<float, false, true> : &NEIm2ColKernel::run_im2col<float, true, true>;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> <span class="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  _func = (!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.has_padding()) ? &NEIm2ColKernel::run_im2col<float16_t, false, true> : &NEIm2ColKernel::run_im2col<float16_t, true, true>;</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */</span><span class="preprocessor"></span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  _func = (!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.has_padding()) ? &NEIm2ColKernel::run_im2col<qasymm8_t, false, true> : &NEIm2ColKernel::run_im2col<qasymm8_t, true, true>;</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Data type not supported"</span>);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  }</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  }</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  {</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keywordflow">switch</span>(_input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  {</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  _func = (!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.has_padding()) ? &NEIm2ColKernel::run_im2col<float, false, false> : &NEIm2ColKernel::run_im2col<float, true, false>;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> <span class="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  _func = (!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.has_padding()) ? &NEIm2ColKernel::run_im2col<float16_t, false, false> : &NEIm2ColKernel::run_im2col<float16_t, true, false>;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */</span><span class="preprocessor"></span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  _func = (!<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.has_padding()) ? &NEIm2ColKernel::run_im2col<qasymm8_t, false, false> : &NEIm2ColKernel::run_im2col<qasymm8_t, true, false>;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Data type not supported"</span>);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  }</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="comment">// Configure kernel window</span></div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keyword">auto</span> win_config = <a class="code" href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">validate_and_configure_window</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(win_config.first);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  INEKernel::configure(win_config.second);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> }</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a"> 420</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">NEIm2ColKernel::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &kernel_dims, <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="l00421"></a><span class="lineno"> 421</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</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>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_arguments(input, output, kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">validate_and_configure_window</a>(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get(), output-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get(), kernel_dims, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>).first);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span> }</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a112b35dd205c62ea6ed1447ef226da82"> 428</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a112b35dd205c62ea6ed1447ef226da82">NEIm2ColKernel::run</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_thread_info.xhtml">ThreadInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="_validate_8h.xhtml#a1b35b0d258183cf9ef36adf684d0b88c">ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL</a>(<span class="keyword">this</span>);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <a class="code" href="_validate_8h.xhtml#a6eb9ce82815fe429250189da7592ba75">ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">INEKernel::window</a>(), <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  (this->*_func)(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> }</div><div class="ttc" id="namespacearm__compute_xhtml_a0bee325b210f81bb89fe1f9e15badf9c"><div class="ttname"><a href="namespacearm__compute.xhtml#a0bee325b210f81bb89fe1f9e15badf9c">arm_compute::is_data_type_quantized</a></div><div class="ttdeci">bool is_data_type_quantized(DataType dt)</div><div class="ttdoc">Check if a given data type is of quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01010">Utils.h:1010</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00261">Error.h:261</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_kernel_xhtml_ad34a46f53686c12a5c5e717cc9617fb6"><div class="ttname"><a href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">arm_compute::IKernel::window</a></div><div class="ttdeci">const Window & window() const</div><div class="ttdoc">The maximum window the kernel can be executed on.</div><div class="ttdef"><b>Definition:</b> <a href="_i_kernel_8cpp_source.xhtml#l00028">IKernel.cpp:28</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="_i_tensor_8h_xhtml"><div class="ttname"><a href="_i_tensor_8h.xhtml">ITensor.h</a></div></div> |
| <div class="ttc" id="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="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_abbe3399a66c35c3c353030cd0a84c936"><div class="ttname"><a href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00387">Utils.cpp:387</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad6630777dc2d315531f1e0b02491051f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">arm_compute::validate_and_configure_window</a></div><div class="ttdeci">std::pair< Status, Window > validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_depthwise_convolution_layer_native_kernel_8cpp_source.xhtml#l00221">NEDepthwiseConvolutionLayerNativeKernel.cpp:221</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_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="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00791">Validate.h:791</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="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00327">Error.h:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image's dimensions with a start, end and step.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00075">Window.h:75</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="_tensor_info_8h_xhtml"><div class="ttname"><a href="_tensor_info_8h.xhtml">TensorInfo.h</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_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab7980fa5ee693e3282a76da047a1c3b5"><div class="ttname"><a href="namespacearm__compute.xhtml#ab7980fa5ee693e3282a76da047a1c3b5">arm_compute::calculate_max_window</a></div><div class="ttdeci">Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps=Steps(), bool skip_border=false, BorderSize border_size=BorderSize())</div><div class="ttdoc">Calculate the maximum window for a given tensor shape and border setting.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_helpers_8cpp_source.xhtml#l00028">Helpers.cpp:28</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="classarm__compute_1_1_i_tensor_info_xhtml_a9586081a29fceb532ab270bd843abee6"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a9586081a29fceb532ab270bd843abee6">arm_compute::ITensorInfo::set_valid_region</a></div><div class="ttdeci">virtual void set_valid_region(const ValidRegion &valid_region)=0</div><div class="ttdoc">Set the valid region of the tensor.</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="classarm__compute_1_1_size2_d_xhtml_a02afeaaf8574e7a78d6b466ff2695052"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">arm_compute::Size2D::height</a></div><div class="ttdeci">size_t height</div><div class="ttdoc">Height of the image region or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00093">Size2D.h:93</a></div></div> |
| <div class="ttc" id="_c_p_p_2_validate_8h_xhtml_ad2633f3560322e1f8d926949dec1b730"><div class="ttname"><a href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(tensor)</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_validate_8h_source.xhtml#l00071">Validate.h:71</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="_size2_d_8h_xhtml"><div class="ttname"><a href="_size2_d_8h.xhtml">Size2D.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div> |
| <div class="ttc" id="_c_p_p_2_validate_8h_xhtml"><div class="ttname"><a href="_c_p_p_2_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a27e4638546c88b8916f967e6e54480a9"><div class="ttname"><a href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00443">Validate.h:443</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="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="classarm__compute_1_1_steps_xhtml"><div class="ttname"><a href="classarm__compute_1_1_steps.xhtml">arm_compute::Steps</a></div><div class="ttdoc">Class to describe a number of elements in each dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_steps_8h_source.xhtml#l00040">Steps.h:40</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a2270b3e1d20651d2d8341c858c890830"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">arm_compute::test::validation::num_groups</a></div><div class="ttdeci">const unsigned int num_groups</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">Im2Col.cpp:148</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item.</div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a86084036bd3851575ef871ad5bf079a7"><div class="ttname"><a href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(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#l00214">Error.h:214</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_1_1cast_xhtml_a2ea3d1fc01a3a442900249ca182ffa5e"><div class="ttname"><a href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">arm_compute::utils::cast::U</a></div><div class="ttdeci">U</div><div class="ttdef"><b>Definition:</b> <a href="_saturate_cast_8h_source.xhtml#l00057">SaturateCast.h:57</a></div></div> |
| <div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< 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_n_e_im2_col_kernel_xhtml_aec5fb874227c941e8ac14de3d29e543b"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#aec5fb874227c941e8ac14de3d29e543b">arm_compute::NEIm2ColKernel::NEIm2ColKernel</a></div><div class="ttdeci">NEIm2ColKernel()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00346">NEIm2ColKernel.cpp:346</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00676">Types.h:676</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a9aeced5a5128f60a31ea3e327a45ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">arm_compute::test::validation::has_bias</a></div><div class="ttdeci">const bool has_bias</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00147">Im2Col.cpp:147</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_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="_validate_8h_xhtml_aba910b683652be1f65437ef37a9da2a9"><div class="ttname"><a href="_validate_8h.xhtml#aba910b683652be1f65437ef37a9da2a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00610">Validate.h:610</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_im2_col_kernel_xhtml_a112b35dd205c62ea6ed1447ef226da82"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a112b35dd205c62ea6ed1447ef226da82">arm_compute::NEIm2ColKernel::run</a></div><div class="ttdeci">void run(const Window &window, const ThreadInfo &info) override</div><div class="ttdoc">Execute the kernel on the passed window.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00428">NEIm2ColKernel.cpp:428</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_thread_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_thread_info.xhtml">arm_compute::ThreadInfo</a></div><div class="ttdoc">Information about executing thread and CPU.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_types_8h_source.xhtml#l00225">CPPTypes.h:225</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_size2_d_xhtml_a02bed8590a9ddf520e58a060059518ec"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">arm_compute::Size2D::width</a></div><div class="ttdeci">size_t width</div><div class="ttdoc">Width of the image region or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00092">Size2D.h:92</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a893d17b56b9abc4423ce26e9a24ac5dc"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">arm_compute::Window::DimZ</a></div><div class="ttdeci">static constexpr size_t DimZ</div><div class="ttdoc">Alias for dimension 2 also known as Z dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00047">Window.h:47</a></div></div> |
| <div class="ttc" id="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="_n_e_im2_col_kernel_8h_xhtml"><div class="ttname"><a href="_n_e_im2_col_kernel_8h.xhtml">NEIm2ColKernel.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_im2_col_kernel_xhtml_a585edc13576fe5f51f7cc493751fef52"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a585edc13576fe5f51f7cc493751fef52">arm_compute::NEIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00351">NEIm2ColKernel.cpp:351</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="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_xhtml_a5002bf7ec46d52971f9526e94172cfee"><div class="ttname"><a href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00122">Helpers.inl:122</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_valid_region_xhtml"><div class="ttname"><a href="structarm__compute_1_1_valid_region.xhtml">arm_compute::ValidRegion</a></div><div class="ttdoc">Container for valid region of a window.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00174">Types.h:174</a></div></div> |
| <div class="ttc" id="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="classarm__compute_1_1_iterator_xhtml"><div class="ttname"><a href="classarm__compute_1_1_iterator.xhtml">arm_compute::Iterator</a></div><div class="ttdoc">Iterator updated by execute_window_loop for each window element.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00318">Helpers.h:318</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a6eb9ce82815fe429250189da7592ba75"><div class="ttname"><a href="_validate_8h.xhtml#a6eb9ce82815fe429250189da7592ba75">ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00205">Validate.h:205</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00114">Types.h:114</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_im2_col_kernel_xhtml_a4e256965ba7798ffe1358469be661e5a"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">arm_compute::NEIm2ColKernel::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, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEIm2ColKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00420">NEIm2ColKernel.cpp:420</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8a9286d053e9f3a958064e4f3cdd02f7"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7">arm_compute::misc::shape_calculator::compute_im2col_conv_shape</a></div><div class="ttdeci">TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, bool batch_size_on_z, unsigned int num_groups=1)</div><div class="ttdoc">Calculate the im2col output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00513">ShapeCalculator.h:513</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a1b35b0d258183cf9ef36adf684d0b88c"><div class="ttname"><a href="_validate_8h.xhtml#a1b35b0d258183cf9ef36adf684d0b88c">ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00940">Validate.h:940</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
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