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<div class="title">Winograd.cpp</div> </div>
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<a href="reference_2_winograd_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>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<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>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<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>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_winograd_8h.xhtml">Winograd.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2validation_2_helpers_8h.xhtml">tests/validation/Helpers.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tests_2validation_2reference_2_utils_8h.xhtml">tests/validation/reference/Utils.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="preprocessor">#include &lt;cmath&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;<span class="keyword">namespace </span>test</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;{</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;<span class="keyword">namespace </span>validation</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;{</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;<span class="keyword">namespace </span>reference</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;{</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160;{</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;<span class="keywordtype">void</span> initialize_matrix_transform(SimpleTensor&lt;T&gt; &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">src</a>, <span class="keyword">const</span> Size2D &amp;output_tile_size, <span class="keyword">const</span> Size2D &amp;kernel_size, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871e">WinogradTransformType</a> winograd_transform_type)</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;{</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <span class="comment">// Winograd input transform matrices</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> imatrix2x2_3x3[] =</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; 1.0f, 0.0f, -1.0f, 0.0f,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; 0.0f, 1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; 0.0f, -1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; 0.0f, 1.0f, 0.0f, -1.0f</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; };</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> imatrix4x4_3x3[] =</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; 4.0f, 0.0f, -5.0f, 0.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; 0.0f, -4.0f, -4.0f, 1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; 0.0f, 4.0f, -4.0f, -1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; 0.0f, -2.0f, -1.0f, 2.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; 0.0f, 2.0f, -1.0f, -2.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; 0.0f, 4.0f, 0.0f, -5.0f, 0.0f, 1.0f,</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; };</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> imatrix4x4_5x5[] =</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; 1.f, 0.f, -21.f / 4.f, 0.f, 21.f / 4.f, 0.f, -1.f, 0.f,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; 0.f, 1.f, 1.f, -17.f / 4.f, -17.f / 4.f, 1.f, 1.f, 0.f,</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; 0.f, -1.f, 1.f, 17.f / 4.f, -17.f / 4.f, -1.f, 1.f, 0.f,</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; 0.f, 1.f / 2.f, 1.f / 4.f, -5.f / 2.f, -5.f / 4.f, 2.f, 1.f, 0.f,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; 0.f, -1.f / 2.f, 1.f / 4.f, 5.f / 2.f, -5.f / 4.f, -2.f, 1.f, 0.f,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; 0.f, 2.f, 4.f, -5.f / 2.f, -5.f, 1.f / 2.f, 1.f, 0.f,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; 0.f, -2.f, 4.f, 5.f / 2.f, -5.f, -1.f / 2.f, 1.f, 0.f,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; 0.f, -1.f, 0.f, 21.f / 4.f, 0.f, -21.f / 4.f, 0.f, 1.f</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; };</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> imatrix2x1_7x7[] =</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; {</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; -36.0f, 0.0f, 49.0f, 0.0f, -14.0f, 0.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; 0.0f, -36.0f, 36.0f, 13.0f, -13.0f, -1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; 0.0f, 36.0f, 36.0f, -13.0f, -13.0f, 1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; 0.0f, -18.0f, 9.0f, 20.0f, -10.0f, -2.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; 0.0f, 18.0f, 9.0f, -20.0f, -10.0f, 2.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; 0.0f, -12.0f, 4.0f, 15.0f, -5.0f, -3.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; 0.0f, 12.0f, 4.0f, -15.0f, -5.0f, 3.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; 0.0f, -36.0f, 0.0f, 49.0f, 0.0f, -14.0f, 0.0f, 1.0f</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; };</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <span class="comment">// ------------------------------------------</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="comment">// Winograd filter transform matrices</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> fmatrix2x2_3x3[] =</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; 1.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; 0.5f, -0.5f, 0.5f,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; 0.0f, 0.0f, 1.0f</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; };</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> fmatrix4x4_3x3[] =</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; 0.25f, 0.0f, 0.0f,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; -1.0f / 6.0f, -1.0f / 6.0f, -1.0f / 6.0f,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; -1.0f / 6.0f, 1.0f / 6.0f, -1.0f / 6.0f,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; 1.0f / 24.0f, 1.0f / 12.0f, 1.0f / 6.0f,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; 1.0f / 24.0f, -1.0f / 12.0f, 1.0f / 6.0f,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; 0.0f, 0.0f, 1.0f</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; };</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> fmatrix4x4_5x5[] =</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; 1.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; -2.0f / 9.0f, 2.0f / 9.0f, -2.0f / 9.0f, 2.0f / 9.0f, -2.0f / 9.0f,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; 1.0f / 90.0f, 1.0f / 45.0f, 2.0f / 45.0f, 4.0f / 45.0f, 8.0f / 45.0f,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; 1.0f / 90.0f, -1.0f / 45.0f, 2.0f / 45.0f, -4.0f / 45.0f, 8.0f / 45.0f,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; 4.0f / 45.0f, 2.0f / 45.0f, 1.0f / 45.0f, 1.0f / 90.0f, 1.0f / 180.0f,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; 4.0f / 45.0f, -2.0f / 45.0f, 1.0f / 45.0f, -1.0f / 90.0f, 1.0f / 180.0f,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 1.0f</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; };</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> fmatrix2x1_7x7[] =</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; -1.0f / 36.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; 1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; -1.0f / 120.0f, 1.0f / 60.0f, -1.0f / 30.0f, 1.0f / 15.0f, -2.0f / 15.0f, 4.0f / 15.0f, -8.0f / 15.0f,</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; -1.0f / 120.0f, -1.0f / 60.0f, -1.0f / 30.0f, -1.0f / 15.0f, -2.0f / 15.0f, -4.0f / 15.0f, -8.0f / 15.0f,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; 1.0f / 720.0f, -1.0f / 240.0f, 1.0f / 80.0f, -3.0f / 80.0f, 9.0f / 80.0f, -27.0f / 80.0f, 81.0f / 80.0f,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; 1.0f / 720.0f, 1.0f / 240.0f, 1.0f / 80.0f, 3.0f / 80.0f, 9.0f / 80.0f, 27.0f / 80.0f, 81.0f / 80.0f,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; };</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <span class="comment">// ------------------------------------------</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <span class="comment">// Winograd output transform matrices</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> omatrix2x2_3x3[] =</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; 1.0f, 1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; 0.0f, 1.0f, -1.0f, -1.0f</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; };</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> omatrix4x4_3x3[] =</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; 0.0f, 1.0f, -1.0f, 2.0f, -2.0f, 0.0f,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; 0.0f, 1.0f, 1.0f, 4.0f, 4.0f, 0.0f,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; 0.0f, 1.0f, -1.0f, 8.0f, -8.0f, 1.0f</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; };</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> omatrix4x4_5x5[] =</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; {</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 8.0f, 0.0f,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; 0.0f, 1.0f, -1.0f, 2.0f, -2.0f, 4.0f, -4.0f, 0.0f,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; 0.0f, 1.0f, 1.0f, 4.0f, 4.0f, 2.0f, 2.0f, 0.0f,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; 0.0f, 1.0f, -1.0f, 8.0f, -8.0f, 1.0f, -1.0f, 1.0f</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; };</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">float</span> omatrix2x1_7x7[] =</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; 0.0f, -1.0f, 1.0f, -2.0f, 2.0f, -3.0f, 3.0f, 1.0f</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; };</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <span class="comment">// ------------------------------------------</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; <span class="keyword">using</span> WinogradKey = std::tuple&lt;std::pair&lt;int, int&gt;, std::pair&lt;int, int&gt;, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871e">WinogradTransformType</a>&gt;;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; <span class="comment">// Key = (Output tile size, Kernel size, Winograd transform type)</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <span class="keyword">static</span> std::map&lt;WinogradKey, const float *&gt; matrix_map =</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 2), std::pair&lt;int, int&gt;(3, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix2x2_3x3 },</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 4), std::pair&lt;int, int&gt;(3, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix4x4_3x3 },</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 1), std::pair&lt;int, int&gt;(3, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix2x2_3x3 },</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 1), std::pair&lt;int, int&gt;(3, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix4x4_3x3 },</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 2), std::pair&lt;int, int&gt;(1, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix2x2_3x3 },</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 4), std::pair&lt;int, int&gt;(1, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix4x4_3x3 },</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 4), std::pair&lt;int, int&gt;(5, 5), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix4x4_5x5 },</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 1), std::pair&lt;int, int&gt;(5, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix4x4_5x5 },</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 1), std::pair&lt;int, int&gt;(7, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix2x1_7x7 },</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 2), std::pair&lt;int, int&gt;(1, 7), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix2x1_7x7 },</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 4), std::pair&lt;int, int&gt;(1, 5), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>), imatrix4x4_5x5 },</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 2), std::pair&lt;int, int&gt;(3, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix2x2_3x3 },</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 4), std::pair&lt;int, int&gt;(3, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix4x4_3x3 },</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 1), std::pair&lt;int, int&gt;(3, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix2x2_3x3 },</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 1), std::pair&lt;int, int&gt;(3, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix4x4_3x3 },</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 2), std::pair&lt;int, int&gt;(1, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix2x2_3x3 },</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 4), std::pair&lt;int, int&gt;(1, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix4x4_3x3 },</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 4), std::pair&lt;int, int&gt;(5, 5), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix4x4_5x5 },</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 1), std::pair&lt;int, int&gt;(5, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix4x4_5x5 },</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 1), std::pair&lt;int, int&gt;(7, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix2x1_7x7 },</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 2), std::pair&lt;int, int&gt;(1, 7), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix2x1_7x7 },</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 4), std::pair&lt;int, int&gt;(1, 5), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>), fmatrix4x4_5x5 },</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 2), std::pair&lt;int, int&gt;(3, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix2x2_3x3 },</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 4), std::pair&lt;int, int&gt;(3, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix4x4_3x3 },</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 1), std::pair&lt;int, int&gt;(3, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix2x2_3x3 },</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 1), std::pair&lt;int, int&gt;(3, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix4x4_3x3 },</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 2), std::pair&lt;int, int&gt;(1, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix2x2_3x3 },</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 4), std::pair&lt;int, int&gt;(1, 3), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix4x4_3x3 },</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 4), std::pair&lt;int, int&gt;(5, 5), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix4x4_5x5 },</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(4, 1), std::pair&lt;int, int&gt;(5, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix4x4_5x5 },</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(2, 1), std::pair&lt;int, int&gt;(7, 1), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix2x1_7x7 },</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 2), std::pair&lt;int, int&gt;(1, 7), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix2x1_7x7 },</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; { WinogradKey(std::pair&lt;int, int&gt;(1, 4), std::pair&lt;int, int&gt;(1, 5), <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>), omatrix4x4_5x5 },</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; };</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <span class="comment">// Find transformation matrix</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; std::map&lt;WinogradKey, const float *&gt;::iterator it;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; it = matrix_map.find(WinogradKey(std::pair&lt;int, int&gt;(output_tile_size.width, output_tile_size.height),</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; std::pair&lt;int, int&gt;(kernel_size.width, kernel_size.height),</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; winograd_transform_type));</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <span class="keywordtype">float</span> <span class="keyword">const</span> *matrix_values = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <span class="keywordflow">if</span>(it != matrix_map.end())</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <span class="comment">// Get matrix pointer</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; matrix_values = it-&gt;second;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; {</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">&quot;Winograd configuration not supported&quot;</span>);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; <span class="comment">// Copy values</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">std::copy</a>(&amp;matrix_values[0], &amp;matrix_values[0] + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">src</a>.num_elements(), &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">src</a>[0]);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160;}</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a09a73d5705389176ff8b7f95946dbc2d"> 233</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a09a73d5705389176ff8b7f95946dbc2d">winograd_input_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160;{</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a5f63b63606dbbbe54474e6e970a6738c">data_layout</a>() != <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; <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#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.convolution_info;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_tile_size;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> out{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() };</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; <span class="comment">// Calculate dimensions for the tile</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tile_w = output_tile_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#a02bed8590a9ddf520e58a060059518ec">width</a> - 1;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tile_h = output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> + kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> - 1;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160;</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; <span class="comment">// Get the maximum dimension from the tile size</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tile_max_dim = std::max(tile_w, tile_h);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tile_dims(tile_max_dim, tile_max_dim);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; <span class="comment">// Simple tensor for the input tile</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> src_tile{ tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() };</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160;</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; <span class="comment">// Simple tensor for the temporary tile</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> tmp_tile{ tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() };</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; <span class="comment">// Simple tensor for the output tile</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> dst_tile{ tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() };</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <span class="comment">// Simple tensor for the transformation matrix</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> matrix{ tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() };</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; <span class="comment">// Simple tensor for the transformation matrix transposed</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> matrix_transposed{ tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>() };</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; <span class="comment">// Initialize matrix for the input transform</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; initialize_matrix_transform(matrix, output_tile_size, kernel_size, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">WinogradTransformType::INPUT</a>);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; <span class="comment">// Transpose matrix</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; transpose_matrix&lt;T&gt;(matrix, matrix_transposed);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> in_w = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().x();</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> in_h = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().y();</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> in_d = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().z();</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> out_d = out.shape().z();</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_batches = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size() / (in_w * in_h * in_d);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> step_x = output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>;</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> step_y = output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <span class="comment">// Compute the number of output tiles along the x and y direction of size &quot;output_tile_size&quot;</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> num_tiles = <a class="code" href="namespacearm__compute.xhtml#a3b0c016b53e97663b39c2f3875f46c24">compute_winograd_convolution_tiles</a>(<a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(in_w, in_h),</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; kernel_size,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; output_tile_size,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_tiles_x = num_tiles.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_tiles_y = num_tiles.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; <span class="comment">// In case of 1D convolution, the input tile has to be partially filled with zeros</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; <span class="keywordtype">int</span> start_x_zero = 0;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; <span class="keywordtype">int</span> start_y_zero = 0;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; <span class="keywordtype">int</span> end_x_zero = 0;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; <span class="keywordtype">int</span> end_y_zero = 0;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <span class="keywordflow">if</span>(output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> == 1)</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; {</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; start_x_zero = 1;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; start_y_zero = 0;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; end_x_zero = tile_max_dim - 1;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; end_y_zero = tile_max_dim;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; }</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; <span class="keywordflow">else</span> <span class="keywordflow">if</span>(output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> == 1)</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; {</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; start_x_zero = 0;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; start_y_zero = 1;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; end_x_zero = tile_max_dim;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; end_y_zero = tile_max_dim - 1;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; }</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; <span class="comment">// Set the anchor and shape of the zeros area</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> anchor_zeros(start_x_zero, start_y_zero);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_zeros(end_x_zero, end_y_zero);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; <span class="comment">// If we have a vertical filter (i.e. 1x3, 1x5,..), we need to take the elements along the y direction (step = width of the output tile)</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> step_y_transf_tile = kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> == 1 ? tile_max_dim : 1;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((num_tiles_x * num_tiles_y) != static_cast&lt;int&gt;(out.shape().y()));</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> b = 0; b &lt; num_batches; ++b)</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; {</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> z = 0; z &lt; in_d; ++z)</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; {</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y &lt; num_tiles_y; ++y)</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; {</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = 0; x &lt; num_tiles_x; ++x)</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; {</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; <span class="keywordtype">int</span> xi = x * step_x - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>.pad_left();</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; <span class="keywordtype">int</span> yi = y * step_y - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>.pad_top();</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; <span class="comment">// Get the tile from the input tensor</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; get_tile&lt;T&gt;(in, src_tile, <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(xi, yi, z, b));</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; <span class="comment">// Fill partially with zeros in case of 1D convolution</span></div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; zeros&lt;T&gt;(src_tile, anchor_zeros, shape_zeros);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; <span class="comment">// Compute the transformation</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; matrix_multiply&lt;T&gt;(matrix, src_tile, tmp_tile);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; matrix_multiply&lt;T&gt;(tmp_tile, matrix_transposed, dst_tile);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; <span class="comment">// Store the output tile across the channels</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i &lt; out_d; ++i)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; <span class="keywordtype">int</span> xo = z;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; <span class="keywordtype">int</span> yo = x + y * num_tiles_x;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; out[<a class="code" href="namespacearm__compute.xhtml#ad95e1c14c3007ca18950bf8f4c5a5c93">coords2index</a>(out.shape(), <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(xo, yo, i, b))] = dst_tile[i * step_y_transf_tile];</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; }</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; }</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; }</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; }</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; <span class="keywordflow">return</span> out;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160;}</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160;</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00357"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ae1720f2a51d1415a9c5afbf2a5c2749f"> 357</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ae1720f2a51d1415a9c5afbf2a5c2749f">winograd_filter_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160;{</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a5f63b63606dbbbe54474e6e970a6738c">data_layout</a>() != <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, <span class="stringliteral">&quot;Only supported NCHW data format&quot;</span>);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160;</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; <span class="comment">// Create reference</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> out{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_tile_size;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; <span class="comment">// Calculate dimensions for the tile</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_tile_w = output_tile_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#a02bed8590a9ddf520e58a060059518ec">width</a> - 1;</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_tile_h = output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> + kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> - 1;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_tile_area = input_tile_w * input_tile_h;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; <span class="comment">// Get the maximum dimension from the filter size</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_max_dim = std::max(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="l00374"></a><span class="lineno"> 374</span>&#160;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; <span class="comment">// Get the maximum dimension from the input tile</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_tile_max_dim = std::max(input_tile_w, input_tile_h);</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160;</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; <span class="comment">// Simple tensor for the input tile</span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> input_tile{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(kernel_max_dim, kernel_max_dim), in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160;</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; <span class="comment">// Simple tensor for the transformation matrix</span></div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> trans_matrix{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(kernel_max_dim, input_tile_max_dim), in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; <span class="comment">// Simple tensor for the transformation matrix transpose</span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> trans_matrix_transposed{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_tile_max_dim, kernel_max_dim), in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160;</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; <span class="comment">// Simple tensor for the temporary tile</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> tmp_tile{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(kernel_max_dim, input_tile_max_dim), in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160;</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; <span class="comment">// Simple tensor for the output tile</span></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> transf_tile{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_tile_max_dim, input_tile_max_dim), in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; <span class="comment">// Initialize matrix for the filter transform</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">WinogradTransformType::FILTER</a>);</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; <span class="comment">// Transpose the transformation matrix</span></div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; transpose_matrix&lt;T&gt;(trans_matrix, trans_matrix_transposed);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_channels = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[2];</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_filters = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[3];</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_batches = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size() / (kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">area</a>() * num_channels * num_filters);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; <span class="comment">// If we have a vertical filter (i.e. 1x3, 1x5,..), we need to take the elements along the y direction (step_y_transf_tile = width of the output tile)</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> step_y_transf_tile = kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> == 1 ? input_tile_max_dim : 1;</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> n = 0; n &lt; num_batches; ++n)</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> w = 0; w &lt; num_filters; ++w)</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; {</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> z = 0; z &lt; num_channels; ++z)</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; <span class="comment">// Load the tile from the input tensor</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; get_tile&lt;T&gt;(in, input_tile, <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0, z, w, n));</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; <span class="comment">// First transformation</span></div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; matrix_multiply&lt;T&gt;(trans_matrix, input_tile, tmp_tile);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160; <span class="comment">// Second transformation</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; matrix_multiply&lt;T&gt;(tmp_tile, trans_matrix_transposed, transf_tile);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; <span class="comment">// Store the output tile across the channels</span></div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> output_offset = w + z * num_filters;</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160;</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; <span class="comment">// Store the values across the channels</span></div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; input_tile_area; ++i)</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; {</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; out[output_offset + i * num_filters * num_channels] = transf_tile[i * step_y_transf_tile];</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; }</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; }</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; }</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; }</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160;</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; <span class="keywordflow">return</span> out;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160;}</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00437"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#adacc73fb5c03e7a1273c0c81c8f8dad5"> 437</a></span>&#160;<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#adacc73fb5c03e7a1273c0c81c8f8dad5">winograd_output_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> &amp;b, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160;{</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; <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#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.convolution_info;</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dimensions = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.input_dimensions;</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_tile_size;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; <span class="comment">// Create reference</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> out{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; <span class="comment">// Calculate dimensions for the tiles</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> in_tile_w = output_tile_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#a02bed8590a9ddf520e58a060059518ec">width</a> - 1;</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> in_tile_h = output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> + kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> - 1;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_tile_w = output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_tile_h = output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[2] != (in_tile_w * in_tile_h));</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[0] != out.shape()[<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#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>)]);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; <span class="comment">// Get the maximum dimension from the tile size</span></div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> in_tile_max_dim = std::max(in_tile_w, in_tile_h);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_tile_max_dim = std::max(output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; <span class="comment">// Compute tile dimensions</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; <span class="comment">// Input tile dimensions</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> in_tile_dims(in_tile_max_dim, in_tile_max_dim);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160;</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; <span class="comment">// Output tile dimensions</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> out_tile_dims(out_tile_max_dim, out_tile_max_dim);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160;</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; <span class="comment">// Transformation matrix dimensions</span></div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tr_tile_dims(in_tile_max_dim, out_tile_max_dim);</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160;</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; <span class="comment">// Create tensors</span></div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; <span class="comment">// Simple tensor for the input tile</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> input_tile{ in_tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; <span class="comment">// Simple tensor for the transformation matrix</span></div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> trans_matrix{ tr_tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160;</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; <span class="comment">// Simple tensor for the transformation matrix transpose</span></div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> trans_matrix_transposed{ <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(tr_tile_dims[1], tr_tile_dims[0]), in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; <span class="comment">// Simple tensor for the temporary tile</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> tmp_tile{ tr_tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; <span class="comment">// Simple tensor for the output tile</span></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;T&gt;</a> output_tile{ out_tile_dims, in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1 };</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160;</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; <span class="comment">// Initialize matrix for the output transform</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">WinogradTransformType::OUTPUT</a>);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; <span class="comment">// Transpose the transformation matrix</span></div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; transpose_matrix&lt;T&gt;(trans_matrix, trans_matrix_transposed);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> w_in = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[0];</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> h_in = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[1];</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> c_in = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[2];</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> w_out = out.shape()[0];</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> h_out = out.shape()[1];</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> c_out = out.shape()[2];</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_batches = in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size() / (w_in * h_in * c_in);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; <span class="comment">// Input strides</span></div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stridey_in = w_in;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stridez_in = stridey_in * h_in;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stridew_in = stridez_in * c_in;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160;</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; <span class="comment">// Output strides</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stridey_out = w_out;</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stridez_out = stridey_out * h_out;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> stridew_out = stridez_out * c_out;</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160;</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; <span class="comment">// Compute the number of output tiles along the x and y direction of size &quot;output_tile_size&quot;</span></div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> num_tiles = <a class="code" href="namespacearm__compute.xhtml#a3b0c016b53e97663b39c2f3875f46c24">compute_winograd_convolution_tiles</a>(<a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(input_dimensions.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, input_dimensions.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>),</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160; kernel_size,</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; output_tile_size,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_tiles_x = num_tiles.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_tiles_y = num_tiles.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>;</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(num_tiles_y);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(in.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[1] != static_cast&lt;unsigned int&gt;(num_tiles_x * num_tiles_y));</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160;</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; <span class="comment">// If we have a vertical filter (i.e. 1x3, 1x5,..), we still need to take the elements along the x direction (step_y_transf_tile = 1)</span></div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> step_y_transf_tile = kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> == 1 ? 1 : output_tile.shape()[0];</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160; <span class="comment">// Initialize with zeros the input tile</span></div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; zeros&lt;T&gt;(input_tile, <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0), input_tile.shape());</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> n = 0; n &lt; num_batches; ++n)</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; {</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = 0; y &lt; h_in; ++y)</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; {</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = 0; x &lt; w_in; ++x)</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; {</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; <span class="comment">// Load the input tile tile across the channels of the input tensor</span></div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> z = 0; z &lt; c_in; ++z)</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; input_tile[z] = in[x + (y * stridey_in) + (z * stridez_in) + (n * stridew_in)];</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; }</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160;</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; <span class="comment">// First transformation</span></div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160; matrix_multiply&lt;T&gt;(trans_matrix, input_tile, tmp_tile);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>&#160; <span class="comment">// Second transformation</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>&#160; matrix_multiply&lt;T&gt;(tmp_tile, trans_matrix_transposed, output_tile);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>&#160;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; <span class="comment">// Store the output tile</span></div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> xo = (y % num_tiles_x) * out_tile_w;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> yo = (y / num_tiles_x) * out_tile_h;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> zo = x;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> output_offset = xo + (yo * stridey_out) + (zo * stridez_out) + (n * stridew_out);</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> yi = 0; yi &lt; static_cast&lt;int&gt;(out_tile_h); ++yi)</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; {</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> xi = 0; xi &lt; static_cast&lt;int&gt;(out_tile_w); ++xi)</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; {</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160; <span class="comment">// Check out-of-bound writes</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160; <span class="keywordflow">if</span>((xo + xi &lt; w_out) &amp;&amp; (yo + yi &lt; h_out))</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160; {</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; out[output_offset + yi * stridey_out + xi] = output_tile[xi + yi * step_y_transf_tile];</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>&#160; <span class="comment">// Add bias</span></div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; out[output_offset + yi * stridey_out + xi] += b[zo];</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160; }</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160; }</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160; }</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>&#160; }</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>&#160; }</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>&#160; }</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>&#160;</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>&#160; <span class="keywordflow">return</span> out;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>&#160;}</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>&#160;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ae1720f2a51d1415a9c5afbf2a5c2749f">winograd_filter_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a09a73d5705389176ff8b7f95946dbc2d">winograd_input_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#adacc73fb5c03e7a1273c0c81c8f8dad5">winograd_output_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;float&gt;</a> &amp;b, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ae1720f2a51d1415a9c5afbf2a5c2749f">winograd_filter_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a09a73d5705389176ff8b7f95946dbc2d">winograd_input_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160;<span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#adacc73fb5c03e7a1273c0c81c8f8dad5">winograd_output_transform</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;in, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor&lt;half&gt;</a> &amp;b, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>&#160;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>&#160;} <span class="comment">// namespace reference</span></div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>&#160;} <span class="comment">// namespace validation</span></div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>&#160;} <span class="comment">// namespace arm_compute</span></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_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="tests_2validation_2reference_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2validation_2reference_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871ea50a87f0d71f7221582dad4bf507a0f34">arm_compute::test::validation::reference::WinogradTransformType::OUTPUT</a></div><div class="ttdoc">Winograd output transform.</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#l01907">Types.h:1907</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::test::SimpleTensor::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00358">SimpleTensor.h:358</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00302">Winograd.cpp:302</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div>
<div class="ttc" id="tests_2validation_2_helpers_8h_xhtml"><div class="ttname"><a href="tests_2validation_2_helpers_8h.xhtml">Helpers.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a3b0c016b53e97663b39c2f3875f46c24"><div class="ttname"><a href="namespacearm__compute.xhtml#a3b0c016b53e97663b39c2f3875f46c24">arm_compute::compute_winograd_convolution_tiles</a></div><div class="ttdeci">Size2D compute_winograd_convolution_tiles(const Size2D &amp;in_dims, const Size2D &amp;kernel_size, const Size2D &amp;output_tile_size, const PadStrideInfo &amp;conv_info)</div><div class="ttdoc">Calculate the number of output tiles required by Winograd Convolution layer.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00735">Helpers.h:735</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00321">SimpleTensor.h:321</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_ad9000ce99b9ffcec5722cade36d7e757"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">arm_compute::test::validation::reference::copy</a></div><div class="ttdeci">SimpleTensor&lt; T &gt; copy(const SimpleTensor&lt; T &gt; &amp;src, const TensorShape &amp;output_shape)</div><div class="ttdef"><b>Definition:</b> <a href="reference_2_copy_8cpp_source.xhtml#l00037">Copy.cpp:37</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eae75ab17a550f46adbbe891b819cf951d">arm_compute::test::validation::reference::WinogradTransformType::FILTER</a></div><div class="ttdoc">Winograd filter transform.</div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_adacc73fb5c03e7a1273c0c81c8f8dad5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#adacc73fb5c03e7a1273c0c81c8f8dad5">arm_compute::test::validation::reference::winograd_output_transform</a></div><div class="ttdeci">SimpleTensor&lt; T &gt; winograd_output_transform(const SimpleTensor&lt; T &gt; &amp;in, const SimpleTensor&lt; T &gt; &amp;b, const TensorShape &amp;output_shape, const WinogradInfo &amp;winograd_info)</div><div class="ttdef"><b>Definition:</b> <a href="reference_2_winograd_8cpp_source.xhtml#l00437">Winograd.cpp:437</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="_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="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_ae1720f2a51d1415a9c5afbf2a5c2749f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ae1720f2a51d1415a9c5afbf2a5c2749f">arm_compute::test::validation::reference::winograd_filter_transform</a></div><div class="ttdeci">SimpleTensor&lt; T &gt; winograd_filter_transform(const SimpleTensor&lt; T &gt; &amp;in, const TensorShape &amp;output_shape, const WinogradInfo &amp;winograd_info)</div><div class="ttdef"><b>Definition:</b> <a href="reference_2_winograd_8cpp_source.xhtml#l00357">Winograd.cpp:357</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div>
<div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item.</div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871eaa84cc046d48610b05c21fd3670d0c829">arm_compute::test::validation::reference::WinogradTransformType::INPUT</a></div><div class="ttdoc">Winograd input transform.</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad95e1c14c3007ca18950bf8f4c5a5c93"><div class="ttname"><a href="namespacearm__compute.xhtml#ad95e1c14c3007ca18950bf8f4c5a5c93">arm_compute::coords2index</a></div><div class="ttdeci">int coords2index(const TensorShape &amp;shape, const Coordinates &amp;coord)</div><div class="ttdoc">Convert n-dimensional coordinates into a linear index.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00308">Helpers.inl:308</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00721">Types.h:721</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a5f63b63606dbbbe54474e6e970a6738c"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a5f63b63606dbbbe54474e6e970a6738c">arm_compute::test::SimpleTensor::data_layout</a></div><div class="ttdeci">DataLayout data_layout() const override</div><div class="ttdoc">Data layout of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00352">SimpleTensor.h:352</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="namespacearm__compute_1_1test_1_1validation_xhtml_ae9d2dc29c2789c253406f9b304cc75a8"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">arm_compute::test::validation::src</a></div><div class="ttdeci">cast configure &amp; src</div><div class="ttdef"><b>Definition:</b> <a href="_cast_8cpp_source.xhtml#l00169">Cast.cpp:169</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">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#l00571">Winograd.cpp:571</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="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a5d19c046f7c6ca24d067312183e8871e"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a5d19c046f7c6ca24d067312183e8871e">arm_compute::test::validation::reference::WinogradTransformType</a></div><div class="ttdeci">WinogradTransformType</div><div class="ttdoc">Winograd transform type.</div><div class="ttdef"><b>Definition:</b> <a href="_winograd_8h_source.xhtml#l00040">Winograd.h:40</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">arm_compute::test::SimpleTensor</a></div><div class="ttdoc">Simple tensor object that stores elements in a consecutive chunk of memory.</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00059">SimpleTensor.h:59</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_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="_winograd_8h_xhtml"><div class="ttname"><a href="_winograd_8h.xhtml">Winograd.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a09a73d5705389176ff8b7f95946dbc2d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a09a73d5705389176ff8b7f95946dbc2d">arm_compute::test::validation::reference::winograd_input_transform</a></div><div class="ttdeci">SimpleTensor&lt; T &gt; winograd_input_transform(const SimpleTensor&lt; T &gt; &amp;in, const TensorShape &amp;output_shape, const WinogradInfo &amp;winograd_info)</div><div class="ttdef"><b>Definition:</b> <a href="reference_2_winograd_8cpp_source.xhtml#l00233">Winograd.cpp:233</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="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="classarm__compute_1_1_size2_d_xhtml_a75026dc1fa3840404ae4553010efcd52"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">arm_compute::Size2D::area</a></div><div class="ttdeci">size_t area() const</div><div class="ttdoc">The area of the image or rectangle calculated as (width * height)</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00053">Size2D.h:53</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
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