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| <div class="title">CLWinogradConvolutionLayer.cpp</div> </div> |
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| <a href="_c_l_winograd_convolution_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2018-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_c_l_winograd_convolution_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_i_c_l_tensor_8h.xhtml">arm_compute/core/CL/ICLTensor.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</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="l00029"></a><span class="lineno"> 29</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="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_c_l_scheduler_8h.xhtml">arm_compute/runtime/CL/CLScheduler.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> winograd_output_tile(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &input_dims, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &kernel_dims, <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>)</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>{};</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_max_dim = std::max(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="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="comment">// Check if the input spatial dimensions are smaller than 4</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_input_lt4_nchw = (input_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> <= 4 && input_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> <= 4) && (<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="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">if</span>(kernel_max_dim == 3U)</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">if</span>(kernel_dims == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(3U, 3U))</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  output_tile = is_input_lt4_nchw ? <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(2U, 2U) : <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(4<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>, 4<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_dims == <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(3U, 1U))</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  output_tile = is_input_lt4_nchw ? <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(2U, 1U) : <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(4<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="l00054"></a><span class="lineno"> 54</span>  }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">else</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>  output_tile = is_input_lt4_nchw ? <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 2U) : <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>, 4<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>);</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>  }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(kernel_max_dim == 5U)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  output_tile = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> == 1 ? 1U : 4U,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> == 1 ? 1U : 4U);</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">else</span> <span class="keywordflow">if</span>(kernel_max_dim == 7U)</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>  output_tile = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> == 1 ? 1U : 2U,</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> == 1 ? 1U : 2U);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  }</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="keywordflow">return</span> output_tile;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="keywordtype">bool</span> check_support_fast_math(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &output_tile, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &kernel_size)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// Check if we want to configure a Winograd configuration which requires fast math</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">using</span> WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;</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>  std::vector<WinogradConfiguration> fast_math_winograd =</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)),</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7))</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  };</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">auto</span> p = std::make_pair(std::pair<int, int>(output_tile.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, output_tile.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>),</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::pair<int, int>(kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>));</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">return</span> std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end();</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a331149c5bce4eb9bf1702b3dffd11da9"> 92</a></span> <a class="code" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a331149c5bce4eb9bf1702b3dffd11da9">CLWinogradConvolutionLayer::CLWinogradConvolutionLayer</a>(std::shared_ptr<IMemoryManager> memory_manager)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _original_weights(nullptr),</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  _is_prepared(false)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a795a5e806f4334092fc9f491fe794998"> 98</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a795a5e806f4334092fc9f491fe794998">CLWinogradConvolutionLayer::configure</a>(<a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *biases, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordtype">bool</span> enable_fast_math)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="comment">// Get indices for the width and height</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dims = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width], input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height]);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[idx_width], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[idx_height]);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile = winograd_output_tile(input_dims, kernel_size, 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="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">if</span>(!enable_fast_math)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>); <span class="comment">//disable winograd for fp16 if fast math is false.</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(check_support_fast_math(output_tile, kernel_size), <span class="stringliteral">"This Winograd configuration requires enable_fast_math=true"</span>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a> = <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a>(output_tile,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  kernel_size,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  input_dims,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</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#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>());</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>  _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="comment">// Manage intermediate tensors</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input0);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_batched_mm_output);</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>  <span class="comment">// Do not manage _input1 as it contains the weights</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="comment">// Configure input transform</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  _input_transform.<a class="code" href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml#a703fa268e40647a42ebded33750fe74e">configure</a>(input, &_input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Configure filter transform</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  _filter_transform.<a class="code" href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml#add649717b97789f69133093fe9852bb2">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Configure batched matrix multiply</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  _batched_mm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a34e7b882208ff6720bad2e4f2c7565c5">configure</a>(&_input0, &_input1, <span class="keyword">nullptr</span>, &_batched_mm_output, 1.0f, 0.0f, <a class="code" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">true</span> <span class="comment">/* Reshape weights only for the first run*/</span>, 0, <span class="keyword">false</span>, <span class="keyword">false</span>, <a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">GEMMLowpOutputStageInfo</a>(),</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</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>() == <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>)));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// Configure output transform</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  _output_transform.<a class="code" href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml#a9cbb2ce5deb94bb5e67aaf7bb2c71e73">configure</a>(&_batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// Allocate temporary tensors</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  _input0.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  _batched_mm_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> }</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a1c5a3dc6ea10d1f68d76064b82b8b5c2"> 149</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a1c5a3dc6ea10d1f68d76064b82b8b5c2">CLWinogradConvolutionLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, <span class="keywordtype">bool</span> enable_fast_math)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// Get indeces for the width and height</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(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="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(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="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="comment">// Input shape, kernel size and output tile</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dims = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width], input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height]);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->tensor_shape()[idx_width], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->tensor_shape()[idx_height]);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile = winograd_output_tile(input_dims, kernel_size, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>());</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>  <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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_left() > (kernel_size.x() / 2u)) || (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_right() > (kernel_size.x() / 2u))), <span class="stringliteral">"Winograd only supports padding up to half kernel size"</span>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_top() > (kernel_size.y() / 2u)) || (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.pad_bottom() > (kernel_size.y() / 2u))), <span class="stringliteral">"Winograd only supports padding up to half kernel size"</span>);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="comment">// Check if the Winograd configuration requires fast math</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">if</span>(!enable_fast_math)</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>  <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#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>); <span class="comment">//disable winograd for fp16 if fast math is false.</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(check_support_fast_math(output_tile, kernel_size), <span class="stringliteral">"This Winograd configuration requires enable_fast_math=true"</span>);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  }</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="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a> = <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a>(output_tile,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  kernel_size,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  input_dims,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>());</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">// Validate input transform</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input0_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">misc::shape_calculator::compute_winograd_input_transform_shape</a>(*input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input0 = input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(input0_shape);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">CLWinogradInputTransform::validate</a>(input, &input0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>));</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>  <span class="comment">// Validate filter transform</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input1_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">misc::shape_calculator::compute_winograd_filter_transform_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input1 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->clone()->set_tensor_shape(input1_shape);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">CLWinogradFilterTransformKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &input1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>));</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="comment">// Validate batched matrix multiply</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> batched_mm_output_shape = input0.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>();</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  batched_mm_output_shape[0] = input1.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()[0];</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> batched_mm_output = input0.<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">clone</a>()->set_tensor_shape(batched_mm_output_shape);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">CLGEMM::validate</a>(&input0, &input1, <span class="keyword">nullptr</span>, &batched_mm_output, 1.0f, 0.0f, <a class="code" href="classarm__compute_1_1_g_e_m_m_info.xhtml">GEMMInfo</a>(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">true</span> <span class="comment">/* Reshape weights only for the first run*/</span>, 0, <span class="keyword">false</span>, <span class="keyword">false</span>,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">GEMMLowpOutputStageInfo</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#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>))));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="comment">// Configure output transform</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml#a046b6ea49a42b3303fca3785ec0ce04a">CLWinogradOutputTransformKernel::validate</a>(&batched_mm_output, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>));</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 200</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">CLWinogradConvolutionLayer::run</a>()</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="comment">// Run input transform</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  _input_transform.<a class="code" href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="comment">// Run batched matrix multiplication</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  _batched_mm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">// Run output transform</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_output_transform);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 216</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">CLWinogradConvolutionLayer::prepare</a>()</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">if</span>(!_is_prepared)</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>  <span class="comment">// Run filter transform and mark original weights as unused</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  _input1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_filter_transform, <span class="keyword">false</span>);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  _original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="comment">// Prepare GEMM and release reshaped weights if marked unused by CLGEMM</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  _batched_mm.<a class="code" href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">if</span>(!_input1.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>())</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  _input1.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-><a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  }</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>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">queue</a>().finish();</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> }</div><div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLGEMM::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00632">CLGEMM.cpp:632</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a04249f91ec2964d21a91bb7038821000"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd input transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00631">ShapeCalculator.h:631</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_filter_transform_kernel_xhtml_a6d3d1d4d66f89908455bc5f05e259ce9"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">arm_compute::CLWinogradFilterTransformKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWinogradFilterTransfor...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_filter_transform_kernel_8cpp_source.xhtml#l00133">CLWinogradFilterTransformKernel.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_output_transform_kernel_xhtml_a046b6ea49a42b3303fca3785ec0ce04a"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml#a046b6ea49a42b3303fca3785ec0ce04a">arm_compute::CLWinogradOutputTransformKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info=ActivationLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWinogradOutputTransfor...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_output_transform_kernel_8cpp_source.xhtml#l00222">CLWinogradOutputTransformKernel.cpp:222</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_afbc359bde9be72a6edab175978e56662"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">arm_compute::TensorInfo::clone</a></div><div class="ttdeci">std::unique_ptr< ITensorInfo > clone() const override</div><div class="ttdoc">Provide a clone of the current object of class T.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00306">TensorInfo.cpp:306</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor.cpp:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLGEMM::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00572">CLGEMM.cpp:572</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">ConvolutionLayer.cpp:175</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_convolution_layer_xhtml_a331149c5bce4eb9bf1702b3dffd11da9"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a331149c5bce4eb9bf1702b3dffd11da9">arm_compute::CLWinogradConvolutionLayer::CLWinogradConvolutionLayer</a></div><div class="ttdeci">CLWinogradConvolutionLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr)</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00092">CLWinogradConvolutionLayer.cpp:92</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml">arm_compute::WinogradInfo</a></div><div class="ttdoc">Winograd information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l02043">Types.h:2043</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="classarm__compute_1_1_c_l_winograd_convolution_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLWinogradConvolutionLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00200">CLWinogradConvolutionLayer.cpp:200</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="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</a></div></div> |
| <div class="ttc" id="_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="classarm__compute_1_1_c_l_winograd_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLWinogradConvolutionLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00216">CLWinogradConvolutionLayer.cpp:216</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00330">Winograd.cpp:330</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_filter_transform_kernel_xhtml_add649717b97789f69133093fe9852bb2"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_filter_transform_kernel.xhtml#add649717b97789f69133093fe9852bb2">arm_compute::CLWinogradFilterTransformKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_filter_transform_kernel_8cpp_source.xhtml#l00102">CLWinogradFilterTransformKernel.cpp:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01517">Types.h:1517</a></div></div> |
| <div class="ttc" id="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_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="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="_c_l_scheduler_8h_xhtml"><div class="ttname"><a href="_c_l_scheduler_8h.xhtml">CLScheduler.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICLSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_simple_function_8cpp_source.xhtml#l00037">ICLSimpleFunction.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_output_transform_kernel_xhtml_a9cbb2ce5deb94bb5e67aaf7bb2c71e73"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_output_transform_kernel.xhtml#a9cbb2ce5deb94bb5e67aaf7bb2c71e73">arm_compute::CLWinogradOutputTransformKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info=ActivationLayerInfo())</div><div class="ttdoc">Set the input and output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_output_transform_kernel_8cpp_source.xhtml#l00146">CLWinogradOutputTransformKernel.cpp:146</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="_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="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_lowp_output_stage_info.xhtml">arm_compute::GEMMLowpOutputStageInfo</a></div><div class="ttdoc">GEMMLowp output stage info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01845">Types.h:1845</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_1misc_1_1shape__calculator_xhtml_a25e3751f07d4b2771a05d8d01a7f7620"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd filter transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00608">ShapeCalculator.h:608</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_convolution_layer_xhtml_a1c5a3dc6ea10d1f68d76064b82b8b5c2"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a1c5a3dc6ea10d1f68d76064b82b8b5c2">arm_compute::CLWinogradConvolutionLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info=ActivationLayerInfo(), bool enable_fast_math=false)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWinogradConvolutionLay...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00149">CLWinogradConvolutionLayer.cpp:149</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ae1a643e517f50bf0392fb6516dd7cf67"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">arm_compute::CLScheduler::enqueue</a></div><div class="ttdeci">void enqueue(ICLKernel &kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler.cpp:95</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00789">Validate.h:789</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ad381d1aed28b4b1e1f5a710633934580"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">arm_compute::CLScheduler::queue</a></div><div class="ttdeci">cl::CommandQueue & queue()</div><div class="ttdoc">Accessor for the associated CL command queue.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00102">CLScheduler.h:102</a></div></div> |
| <div class="ttc" id="_c_l_winograd_convolution_layer_8h_xhtml"><div class="ttname"><a href="_c_l_winograd_convolution_layer_8h.xhtml">CLWinogradConvolutionLayer.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00046">IMemoryGroup.h:46</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_tensor.xhtml">arm_compute::ICLTensor</a></div><div class="ttdoc">Interface for OpenCL tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_tensor_8h_source.xhtml#l00042">ICLTensor.h:42</a></div></div> |
| <div class="ttc" id="_i_c_l_tensor_8h_xhtml"><div class="ttname"><a href="_i_c_l_tensor_8h.xhtml">ICLTensor.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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_c_l_g_e_m_m_xhtml_a34e7b882208ff6720bad2e4f2c7565c5"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a34e7b882208ff6720bad2e4f2c7565c5">arm_compute::CLGEMM::configure</a></div><div class="ttdeci">void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Initialise the kernel's inputs and output.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00470">CLGEMM.cpp:470</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::CLTensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00152">CLTensorAllocator.cpp:152</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_convolution_layer_xhtml_a795a5e806f4334092fc9f491fe794998"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_convolution_layer.xhtml#a795a5e806f4334092fc9f491fe794998">arm_compute::CLWinogradConvolutionLayer::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info=ActivationLayerInfo(), bool enable_fast_math=false)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_convolution_layer_8cpp_source.xhtml#l00098">CLWinogradConvolutionLayer.cpp:98</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_g_e_m_m_xhtml_a3493ba7d1f2057740ff5931fa00a44ac"><div class="ttname"><a href="classarm__compute_1_1_c_l_g_e_m_m.xhtml#a3493ba7d1f2057740ff5931fa00a44ac">arm_compute::CLGEMM::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLGEMM.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_g_e_m_m_8cpp_source.xhtml#l00525">CLGEMM.cpp:525</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_e_m_m_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_info.xhtml">arm_compute::GEMMInfo</a></div><div class="ttdoc">GEMM information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01880">Types.h:1880</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_input_transform_xhtml_a703fa268e40647a42ebded33750fe74e"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml#a703fa268e40647a42ebded33750fe74e">arm_compute::CLWinogradInputTransform::configure</a></div><div class="ttdeci">void configure(ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_input_transform_8cpp_source.xhtml#l00033">CLWinogradInputTransform.cpp:33</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00326">Helpers.inl:326</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape & tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00252">TensorInfo.h:252</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_l_winograd_input_transform_xhtml_a6d3d1d4d66f89908455bc5f05e259ce9"><div class="ttname"><a href="classarm__compute_1_1_c_l_winograd_input_transform.xhtml#a6d3d1d4d66f89908455bc5f05e259ce9">arm_compute::CLWinogradInputTransform::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWinogradInputTransform...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_winograd_input_transform_8cpp_source.xhtml#l00041">CLWinogradInputTransform.cpp:41</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="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div> |
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